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71-467: Finding facial landmarks is an important step in facial identification of people in an image. Facial landmarks can also be used to extract information about mood and intention of the person. Methods used fall in to three categories: holistic methods, constrained local model methods, and regression -based methods. Holistic methods are pre-programmed with statistical information on face shape and landmark location coefficients. The classic holistic method

142-432: A is the global minimum. Let S be the number of particles in the swarm, each having a position x i  ∈ ℝ in the search-space and a velocity v i  ∈ ℝ . Let p i be the best known position of particle i and let g be the best known position of the entire swarm. A basic PSO algorithm to minimize the cost function is then: The values b lo and b up represent the lower and upper boundaries of

213-536: A baseline for performance testing of improvements to the technique, as well as to represent PSO to the wider optimization community. Having a well-known, strictly-defined standard algorithm provides a valuable point of comparison which can be used throughout the field of research to better test new advances." The latest is Standard PSO 2011 (SPSO-2011). New and more sophisticated PSO variants are also continually being introduced in an attempt to improve optimization performance. There are certain trends in that research; one

284-469: A basic PSO algorithm are possible. For example, there are different ways to initialize the particles and velocities (e.g. start with zero velocities instead), how to dampen the velocity, only update p i and g after the entire swarm has been updated, etc. Some of these choices and their possible performance impact have been discussed in the literature. A series of standard implementations have been created by leading researchers, "intended for use both as

355-411: A certain formula in each iteration to optimize landmark detection. Holistic Holism is the interdisciplinary idea that systems possess properties as wholes apart from the properties of their component parts. The aphorism "The whole is greater than the sum of its parts", typically attributed to Aristotle , is often given as a glib summary of this proposal. The concept of holism can inform

426-459: A classical problem for the philosophy of language concerning how words convey meaning, there is debate over its validity mostly from two angles of criticism: opposition to compositionality and, especially, instability of meaning. The first claims that meaning holism conflicts with the compositionality of language. Meaning in some languages is compositional in that meaning comes from the structure of an expression's parts. Meaning holism suggests that

497-423: A comprehensive review on theoretical and experimental works on PSO has been published by Bonyadi and Michalewicz. PSO is a metaheuristic as it makes few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. Also, PSO does not use the gradient of the problem being optimized, which means PSO does not require that the optimization problem be differentiable as

568-445: A database or general search. An example of a fashion landmark is the location of the hemline of a dress. Fashion landmark detection is particularly difficult due to the extreme deformation that can occur in clothing. Some classical methods of feature detection such as scale-invariant feature transform have been used in the past. However, it is now more common to use deep learning methods. This has been helped along enormously by

639-445: A finite number of optimization problems. This means a metaheuristic such as PSO cannot be proven correct and this increases the risk of making errors in its description and implementation. A good example of this presented a promising variant of a genetic algorithm (another popular metaheuristic) but it was later found to be defective as it was strongly biased in its optimization search towards similar values for different dimensions in

710-462: A local minimum, thus different topologies have been used to control the flow of information among particles. For instance, in local topologies, particles only share information with a subset of particles. This subset can be a geometrical one – for example "the m nearest particles" – or, more often, a social one, i.e. a set of particles that is not depending on any distance. In such cases, the PSO variant

781-405: A locally oriented search so as to get closer to a (possibly local) optimum. This school of thought has been prevalent since the inception of PSO. This school of thought contends that the PSO algorithm and its parameters must be chosen so as to properly balance between exploration and exploitation to avoid premature convergence to a local optimum yet still ensure a good rate of convergence to

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852-478: A meaningful analysis of one system is indistinguishable from that of the other. There are different conceptions of nonseparability in physics and its exploration is considered to broadly present insight into the ontological problem. In one sense, holism for physics is a perspective about the best way to understand the nature of a physical system. In this sense, holism is the methodological claim that systems are accurately understood according to their properties as

923-418: A nature as a whole beyond its parts. His examples include atoms , cells , or an individual's personality . Smuts discussed this sense of holism in his claim that an individual's body and mind are not completely separated but instead connect and represent the holistic idea of a person. In his second sense, Smuts referred to holism as the cause of evolution. He argued that evolution is neither an accident nor

994-440: A point and prevent divergence of the swarm's particles (particles do not move unboundedly and will converge to somewhere). However, the analyses were criticized by Pedersen for being oversimplified as they assume the swarm has only one particle, that it does not use stochastic variables and that the points of attraction, that is, the particle's best known position p and the swarm's best known position g , remain constant throughout

1065-457: A refinement of the method one can decrease α {\displaystyle \alpha } with each iteration, α n = α 0 γ n {\displaystyle \alpha _{n}=\alpha _{0}\gamma ^{n}} , where n {\displaystyle n} is the number of the iteration and 0 < γ < 1 {\displaystyle 0<\gamma <1}

1136-519: A whole system to creatively respond to environmental stressors, a process in which parts naturally work together to bring the whole into more advanced states. Smuts used Pavlovian studies to argue that the inheritance of behavioral changes supports his idea of creative evolution as opposed to purely accidental development in nature. Smuts believed that this creative process was intrinsic within all physical systems of parts and ruled out indirect, transcendent forces . Finally, Smuts used holism to explain

1207-434: A whole. A methodological reductionist in physics might seek to explain, for example, the behavior of a liquid by examining its component molecules, atoms, ions or electrons. A methodological holist, on the other hand, believes there is something misguided about this approach; one proponent, a condensed matter physicist, puts it: “the most important advances in this area come about by the emergence of qualitatively new concepts at

1278-583: Is biological organization which models biological systems and structures only in terms of their component parts. "The reductionist approach has successfully identified most of the components and many of the interactions but, unfortunately, offers no convincing concepts or methods to understand how system properties emerge...the pluralism of causes and effects in biological networks is better addressed by observing, through quantitative measures, multiple components simultaneously and by rigorous data integration with mathematical models." The objective in systems biology

1349-401: Is a random uniformly distributed vector, L {\displaystyle L} is the typical length of the problem at hand, and β ∼ 0.1 − 0.7 {\displaystyle \beta \sim 0.1-0.7} and α ∼ 0.1 − 0.5 {\displaystyle \alpha \sim 0.1-0.5} are the parameters of the method. As

1420-481: Is also sometimes used in the context of various lifestyle practices, such as dieting , education, and healthcare, to refer to ways of life that either supplement or replace conventional practices. In these contexts, holism is not necessarily a rigorous or well-defined methodology for obtaining a particular lifestyle outcome. It is sometimes simply an adjective to describe practices which account for factors that standard forms of these practices may discount, especially in

1491-456: Is independent and so there are no emergent properties within a language. Additionally, there is meaning molecularism which states that a change in one word alters the meaning of only a relatively small set of other words. The linguistic perspective of meaning holism is traced back to Quine but was subsequently formalized by analytic philosophers Michael Dummett , Jerry Fodor , and Ernest Lepore . While this holistic approach attempts to resolve

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1562-412: Is it brought about by the actions of some transcendent force, such as a God. Smuts criticized writers who emphasized Darwinian concepts of natural selection and genetic variation to support an accidental view of natural processes within the universe. Smuts perceived evolution as the process of nature correcting itself creatively and intentionally. In this way, holism is described as the tendency of

1633-411: Is not necessarily specified in meaning holism, but typically such a change is taken straightforwardly to affect the meaning of every word in the language. In scientific disciplines, reductionism is the opposing viewpoint to holism. But in the context of linguistics or the philosophy of language , reductionism is typically referred to as atomism. Specifically, atomism states that each word's meaning

1704-424: Is required by classic optimization methods such as gradient descent and quasi-newton methods . However, metaheuristics such as PSO do not guarantee an optimal solution is ever found. A basic variant of the PSO algorithm works by having a population (called a swarm) of candidate solutions (called particles). These particles are moved around in the search-space according to a few simple formulae. The movements of

1775-451: Is said to be local best (vs global best for the basic PSO). A commonly used swarm topology is the ring, in which each particle has just two neighbours, but there are many others. The topology is not necessarily static. In fact, since the topology is related to the diversity of communication of the particles, some efforts have been done to create adaptive topologies (SPSO, APSO, stochastic star, TRIBES, Cyber Swarm, and C-PSO ) By using

1846-467: Is the active appearance model (AAM) introduced in 1998. Since then there has been a number of extensions and improvements to the method. These are largely improvements to the fitting algorithm and can be classified into two groups: analytical fitting methods, and learning-based fitting methods. Analytical methods apply nonlinear optimization methods such as the Gauss–Newton algorithm . This algorithm

1917-402: Is the decrease control parameter. PSO has also been applied to multi-objective problems , in which the objective function comparison takes Pareto dominance into account when moving the PSO particles and non-dominated solutions are stored so as to approximate the pareto front. As the PSO equations given above work on real numbers, a commonly used method to solve discrete problems is to map

1988-478: Is the global best position; G ( x → , σ ) {\displaystyle G({\vec {x}},\sigma )} is the normal distribution with the mean x → {\displaystyle {\vec {x}}} and standard deviation σ {\displaystyle \sigma } ; and where | | … | | {\displaystyle ||\dots ||} signifies

2059-436: Is the use of multiple swarms ( multi-swarm optimization ). The multi-swarm approach can also be used to implement multi-objective optimization. Finally, there are developments in adapting the behavioural parameters of PSO during optimization. Another school of thought is that PSO should be simplified as much as possible without impairing its performance; a general concept often referred to as Occam's razor . Simplifying PSO

2130-474: Is to advance models of the interactions in a system. Holistic approaches to modelling have involved cellular modelling strategies, genomic interaction analysis, and phenotype prediction. Systems medicine is a practical approach to systems biology and accepts its holistic assumptions. Systems medicine takes the systems of the human body as made up of a complete whole and uses this as a starting point in its research and, ultimately, treatment. The term holism

2201-427: Is to make a hybrid optimization method using PSO combined with other optimizers, e.g., combined PSO with biogeography-based optimization, and the incorporation of an effective learning method. Another research trend is to try to alleviate premature convergence (that is, optimization stagnation), e.g. by reversing or perturbing the movement of the PSO particles, another approach to deal with premature convergence

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2272-450: Is very slow but better ones have been proposed such as the project out inverse compositional (POIC) algorithm and the simultaneous inverse compositional (SIC) algorithm. Learning-based fitting methods use machine learning techniques to predict the facial coefficients. These can use linear regression , nonlinear regression and other fitting methods. In general, the analytic fitting methods are more accurate and do not need training, while

2343-456: The search-space according to simple mathematical formulae over the particle's position and velocity . Each particle's movement is influenced by its local best known position, but is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions. PSO is originally attributed to Kennedy , Eberhart and Shi and

2414-519: The 20th century coincided with the gradual development of quantum mechanics . Holism in physics is the nonseparability of physical systems from their parts, especially quantum phenomena. Classical physics cannot be regarded as holistic, as the behavior of individual parts represents the whole. However, the state of a system in quantum theory resists a certain kind of reductive analysis. For example, two spatially separated quantum systems are described as " entangled ," or nonseparable from each other, when

2485-410: The PSO method ( below ). The choice of PSO parameters can have a large impact on optimization performance. Selecting PSO parameters that yield good performance has therefore been the subject of much research. To prevent divergence ("explosion") the inertia weight must be smaller than 1. The two other parameters can be then derived thanks to the constriction approach, or freely selected, but

2556-441: The analyses suggest convergence domains to constrain them. Typical values are in [ 1 , 3 ] {\displaystyle [1,3]} . The PSO parameters can also be tuned by using another overlaying optimizer, a concept known as meta-optimization , or even fine-tuned during the optimization, e.g., by means of fuzzy logic. Parameters have also been tuned for various optimization scenarios. The topology of

2627-405: The composition of its physical parts, but that there are concrete properties aside from those of its basic physical parts. Theoretical physicist David Bohm (1917-1992) supports this view head-on. Bohm believed that a complete description of the universe would have to go beyond a simple list of all its particles and their positions, there would also have to be a physical quantum field associated with

2698-522: The concrete (nontranscendent) nature of the universe in general. In his words, holism is "the ultimate synthetic, ordering, organizing, regulative activity in the universe which accounts for all the structural groupings and syntheses in it." Smuts argued that a holistic view of the universe explains its processes and their evolution more effectively than a reductive view. Professional philosophers of science and linguistics did not consider Holism and Evolution seriously upon its initial publication in 1926 and

2769-430: The context of alternative medicine . Particle swarm optimization In computational science , particle swarm optimization ( PSO ) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles , and moving these particles around in

2840-417: The cost function which must be minimized. The function takes a candidate solution as an argument in the form of a vector of real numbers and produces a real number as output which indicates the objective function value of the given candidate solution. The gradient of f is not known. The goal is to find a solution a for which f ( a ) ≤  f ( b ) for all b in the search-space, which would mean

2911-493: The discrete search space to a continuous domain, to apply a classical PSO, and then to demap the result. Such a mapping can be very simple (for example by just using rounded values) or more sophisticated. However, it can be noted that the equations of movement make use of operators that perform four actions: Usually a position and a velocity are represented by n real numbers, and these operators are simply -, *, +, and again +. But all these mathematical objects can be defined in

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2982-514: The features from large datasets of images. By training a CNN on a dataset of images with labeled facial landmarks, the algorithm can learn to detect these landmarks in new images with high accuracy even when they appear in different lighting conditions, at different angles, or in partially occluded views. In particular, solutions based on this approach have achieved real-time efficiency on mobile devices' GPUs and found its usage within augmented reality applications. Evolutionary algorithms at

3053-404: The information already existing in the relationship between p and g , so as to form a leading converging exemplar and to be effective with any PSO topology. The aims are to improve the performance of PSO overall, including faster global convergence, higher solution quality, and stronger robustness. However, such studies do not provide theoretical evidence to actually prove their claims. Without

3124-462: The instability of meaning holism is an acceptable feature from several different angles. In one example, contextual holists make this point simply by suggesting we often do not actually share identical inferential assumptions but instead rely on context to counter differences of inference and support communication. Scientific applications of holism within biology are referred to as systems biology . The opposing analytical approach of systems biology

3195-582: The intermediate or macroscopic levels—concepts which, one hopes, will be compatible with one's information about the microscopic constituents, but which are in no sense logically dependent on it.” This perspective is considered a conventional attitude among contemporary physicists. In another sense, holism is a metaphysical claim that the nature of a system is not determined by the properties of its component parts. There are three varieties of this sense of physical holism. The metaphysical claim does not assert that physical systems involve abstract properties beyond

3266-413: The learning-based fitting methods are faster, but need to be trained. Other extensions to the basic AAM method analyse wavelets in the image rather than pixel intensity. This helps with fitting unseen parts of the face which basic AAM finds troublesome. The purpose of landmark detection in fashion images is for classification purposes. This aids in the retrieval of images with specified features from

3337-409: The meaning of individual words depends on the meaning of other words, forming a large web of interconnections. In general, meaning holism states that the properties which determine the meaning of a word are connected such that if the meaning of one word changes, the meaning of every other word in the web changes as well. The set of words that alter in meaning due to a change in the meaning of some other

3408-467: The meaning of other words, then in order to communicate a message, the sender and the receiver must share an identical set of inferential assumptions or beliefs. If these beliefs were different, meaning may be lost. Many types of communication would be directly affected by the principles of meaning holism such as informative communication, language learning, and communication about psychological states. Nevertheless, some meaning holists maintain that

3479-544: The meaning of words plays an inferential role in the meaning of other words: "pet fish" might infer a meaning of "less than 3 ounces." Since holistic views of meaning assume meaning depends on which words are used and how those words infer meaning onto other words, rather than how they are structured, meaning holism stands in conflict with compositionalism and leaves statements with potentially ambiguous meanings. The second criticism claims that meaning holism makes meaning in language unstable. If some words must be used to infer

3550-487: The methodology for a broad array of scientific fields and lifestyle practices. When applications of holism are said to reveal properties of a whole system beyond those of its parts, these qualities are referred to as emergent properties of that system. Holism in all contexts is often placed in opposition to reductionism , a dominant notion in the philosophy of science that systems containing parts contain no unique properties beyond those parts. Proponents of holism consider

3621-459: The minimal necessary modeling assumptions. Convergence to a local optimum has been analyzed for PSO in and. It has been proven that PSO needs some modification to guarantee finding a local optimum. This means that determining the convergence capabilities of different PSO algorithms and parameters still depends on empirical results. One attempt at addressing this issue is the development of an "orthogonal learning" strategy for an improved use of

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3692-441: The need for a trade-off between convergence ('exploitation') and divergence ('exploration'), an adaptive mechanism can be introduced. Adaptive particle swarm optimization (APSO) features better search efficiency than standard PSO. APSO can perform global search over the entire search space with a higher convergence speed. It enables automatic control of the inertia weight, acceleration coefficients, and other algorithmic parameters at

3763-494: The norm of a vector. Another simpler variant is the accelerated particle swarm optimization (APSO), which also does not need to use velocity and can speed up the convergence in many applications. A simple demo code of APSO is available. In this variant of PSO one dispences with both the particle's velocity and the particle's best position. The particle position is updated according to the following rule, where u → {\displaystyle {\vec {u}}}

3834-412: The optimization process. However, it was shown that these simplifications do not affect the boundaries found by these studies for parameter where the swarm is convergent. Considerable effort has been made in recent years to weaken the modeling assumption utilized during the stability analysis of PSO, with the most recent generalized result applying to numerous PSO variants and utilized what was shown to be

3905-470: The optimum. This belief is the precursor of many PSO variants, see below . Another school of thought is that the behaviour of a PSO swarm is not well understood in terms of how it affects actual optimization performance, especially for higher-dimensional search-spaces and optimization problems that may be discontinuous, noisy, and time-varying. This school of thought merely tries to find PSO algorithms and parameters that cause good performance regardless of how

3976-405: The particles are guided by their own best-known position in the search-space as well as the entire swarm's best-known position. When improved positions are being discovered these will then come to guide the movements of the swarm. The process is repeated and by doing so it is hoped, but not guaranteed, that a satisfactory solution will eventually be discovered. Formally, let f : ℝ  → ℝ be

4047-440: The positions of the particles using the following simple rule, where x → i {\displaystyle {\vec {x}}_{i}} , p → i {\displaystyle {\vec {p}}_{i}} are the position and the best position of the particle i {\displaystyle i} ; g → {\displaystyle {\vec {g}}}

4118-1138: The properties of those particles guiding their trajectories. Bohm's ontological holism concerning the nature of whole physical systems was literal. But Niels Bohr (1885-1962), on the other hand, held ontological holism from an epistemological angle, rather than a literal one. Bohr saw an observational apparatus to be a part of a system under observation, besides the basic physical parts themselves. His theory agrees with Bohm that whole systems were not merely composed of their parts and it identifies properties such as position and momentum as those of whole systems beyond those of its components. But Bohr states that these holistic properties are only meaningful in experimental contexts when physical systems are under observation and that these systems, when not under observation, cannot be said to have meaningful properties, even if these properties took place outside our observation. While Bohr claims these holistic properties exist only insofar as they can be observed, Bohm took his ontological holism one step further by claiming these properties must exist regardless . Semantic holism suggests that

4189-441: The publication of a number of large fashion datasets that can be used for training. These methods include regression-based models, constraint-based models, and attentive models. The particular problems of fashion landmark detection (deformation) have led to pose estimation models which detect and take into account the pose of the model wearing the clothes. There are several algorithms for locating landmarks in images. Nowadays

4260-413: The ring topology, PSO can attain generation-level parallelism, significantly enhancing the evolutionary speed. There are several schools of thought as to why and how the PSO algorithm can perform optimization. A common belief amongst researchers is that the swarm behaviour varies between exploratory behaviour, that is, searching a broader region of the search-space, and exploitative behaviour, that is,

4331-506: The run time, thereby improving the search effectiveness and efficiency at the same time. Also, APSO can act on the globally best particle to jump out of the likely local optima. However, APSO will introduce new algorithm parameters, it does not introduce additional design or implementation complexity nonetheless. Besides, through the utilization of a scale-adaptive fitness evaluation mechanism, PSO can efficiently address computationally expensive optimization problems. Numerous variants of even

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4402-432: The search for emergent properties within systems to be demonstrative of their perspective. The term "holism" was coined by Jan Smuts (1870–1950) in his 1926 book Holism and Evolution . While he never assigned a consistent meaning to the word, Smuts used holism to represent at least three features of reality. First, holism claims that every scientifically measurable thing, either physical or psychological, does possess

4473-424: The search space, which happened to be the optimum of the benchmark problems considered. This bias was because of a programming error, and has now been fixed. Initialization of velocities may require extra inputs. The Bare Bones PSO variant has been proposed in 2003 by James Kennedy, and does not need to use velocity at all. In this variant of PSO one dispences with the velocity of the particles and instead updates

4544-420: The search-space respectively. The w parameter is the inertia weight. The parameters φ p and φ g are often called cognitive coefficient and social coefficient. The termination criterion can be the number of iterations performed, or a solution where the adequate objective function value is found. The parameters w, φ p , and φ g are selected by the practitioner and control the behaviour and efficacy of

4615-449: The swarm behaviour can be interpreted in relation to e.g. exploration and exploitation. Such studies have led to the simplification of the PSO algorithm, see below . In relation to PSO the word convergence typically refers to two different definitions: Convergence of the sequence of solutions has been investigated for PSO. These analyses have resulted in guidelines for selecting PSO parameters that are believed to cause convergence to

4686-413: The swarm defines the subset of particles with which each particle can exchange information. The basic version of the algorithm uses the global topology as the swarm communication structure. This topology allows all particles to communicate with all the other particles, thus the whole swarm share the same best position g from a single particle. However, this approach might lead the swarm to be trapped into

4757-653: The task usually is solved using Artificial Neural Networks and especially Deep Learning algorithms, but evolutionary algorithms such as particle swarm optimization can also be useful to perform this task. Deep learning has had a significant impact on autonomous facial landmark detection by enabling more accurate and efficient detection of landmarks in real-world photos. With traditional computer vision techniques, detecting facial landmarks could be challenging due to variations in lighting, head position, and occlusion, but Convolutional Neural Networks (CNNs), have revolutionized landmark detection by allowing computers to learn

4828-430: The training stage try to learn the method of correct determination of landmarks. This phase is an iterative process and, accordingly, is performed in several iterations. As a result of the completion of the last iteration, a system will be obtained that can correctly determine the landmark with a certain accuracy. In the particle swarm optimization method, there are particles that search for landmarks, and each of them uses

4899-436: The work has received criticism for a lack of theoretical coherence. Some biological scientists, however, did offer favorable assessments shortly after its first print. Over time, the meaning of the word holism became most closely associated with Smuts' first conception of the term, yet without any metaphysical commitments to monism , dualism , or similar concepts which can be inferred from his work. The advent of holism in

4970-405: Was first intended for simulating social behaviour , as a stylized representation of the movement of organisms in a bird flock or fish school . The algorithm was simplified and it was observed to be performing optimization. The book by Kennedy and Eberhart describes many philosophical aspects of PSO and swarm intelligence . An extensive survey of PSO applications is made by Poli . In 2017,

5041-424: Was originally suggested by Kennedy and has been studied more extensively, where it appeared that optimization performance was improved, and the parameters were easier to tune and they performed more consistently across different optimization problems. Another argument in favour of simplifying PSO is that metaheuristics can only have their efficacy demonstrated empirically by doing computational experiments on

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