Learning spatio-temporal representations for action recognition: A genetic programming approach

Extracting discriminative and robust features from video sequences is the first and most critical step in human action recognition. In this paper, instead of using handcrafted features, we automatically learn spatio-temporal motion features for action recognition. This is achieved via an evolutionary method, i.e., genetic programming (GP), which evolves the motion feature descriptor on a population of primitive 3D operators (e.g., 3D-Gabor and wavelet). In this way, the scale and shift invariant features can be effectively extracted from both color and optical flow sequences. We intend to learn data adaptive descriptors for different datasets with multiple layers, which makes fully use of the knowledge to mimic the physical structure of the human visual cortex for action recognition and simultaneously reduce the GP searching space to effectively accelerate the convergence of optimal solutions. In our evolutionary architecture, the average cross-validation classification error, which is calculated by an support-vector-machine classifier on the training set, is adopted as the evaluation criterion for the GP fitness function. After the entire evolution procedure finishes, the best-so-far solution selected by GP is regarded as the (near-)optimal action descriptor obtained. The GP-evolving feature extraction method is evaluated on four popular action datasets, namely KTH, HMDB51, UCF YouTube, and Hollywood2. Experimental results show that our method significantly outperforms other types of features, either hand-designed or machine-learned

Genetic Programming for Object Detection : a Two-Phase Approach with an Improved Fitness Function

This paper describes two innovations that improve the efficiency and effectiveness of a genetic programming approach to object detection problems. The approach uses genetic programming to construct object detection programs that are applied, in a moving window fashion, to the large images to locate the objects of interest. The first innovation is to break the GP search into two phases with the first phase applied to a selected subset of the training data, and a simplified fitness function. The second phase is initialised with the programs from the first phase, and uses the full set of training data with a complete fitness function to construct the final detection programs. The second innovation is to add a program size component to the fitness function. This approach is examined and compared with a neural network approach on three object detection problems of increasing difficulty. The results suggest that the innovations increase both the effectiveness and the efficiency of the genetic programming search, and also that the genetic programming approach outperforms a neural network approach for the most difficult data set in terms of the object detection accuracy

Progressive insular cooperative genetic programming algorithm for multiclass classification

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsIn contrast to other types of optimisation algorithms, Genetic Programming (GP) simultaneously optimises a group of solutions for a given problem. This group is named population, the algorithm iterations are named generations and the optimisation is named evolution as a reference o the algorithm’s inspiration in Darwin’s theory on the evolution of species. When a GP algorithm uses a one-vs-all class comparison for a multiclass classification (MCC) task, the classifiers for each target class (specialists) are evolved in a subpopulation and the final solution of the GP is a team composed of one specialist classifier of each class. In this scenario, an important question arises: should these subpopulations interact during the evolution process or should they evolve separately? The current thesis presents the Progressively Insular Cooperative (PIC) GP, a MCC GP in which the level of interaction between specialists for different classes changes through the evolution process. In the first generations, the different specialists can interact more, but as the algorithm evolves, this level of interaction decreases. At a later point in the evolution process, controlled through algorithm parameterisation, these interactions can be eliminated. Thus, in the beginning of the algorithm there is more cooperation among specialists of different classes, favouring search space exploration. With elimination of cooperation, search space exploitation is favoured. In this work, different parameters of the proposed algorithm were tested using the Iris dataset from the UCI Machine Learning Repository. The results showed that cooperation among specialists of different classes helps the improvement of classifiers specialised in classes that are more difficult to discriminate. Moreover, the independent evolution of specialist subpopulations further benefits the classifiers when they already achieved good performance. A combination of the two approaches seems to be beneficial when starting with subpopulations of differently performing classifiers. The PIC GP also presented great performance for the more complex Thyroid and Yeast datasets of the same repository, achieving similar accuracy to the best values found in literature for other MCC models.Diferente de outros algoritmos de otimiação computacional, o algoritmo de Programação Genética PG otimiza simultaneamente um grupo de soluções para um determinado problema. Este grupo de soluções é chamado população, as iterações do algoritmo são chamadas de gerações e a otimização é chamada de evolução em alusão à inspiração do algoritmo na teoria da evolução das espécies de Darwin. Quando o algoritmo GP utiliza a abordagem de comparação de classes um-vs-todos para uma classificação multiclasses (CMC), os classificadores específicos para cada classe (especialistas) são evoluídos em subpopulações e a solução final do PG é uma equipe composta por um especialista de cada classe. Neste cenário, surge uma importante questão: estas subpopulações devem interagir durante o processo evolutivo ou devem evoluir separadamente? A presente tese apresenta o algoritmo Cooperação Progressivamente Insular (CPI) PG, um PG CMC em que o grau de interação entre especialistas em diferentes classes varia ao longo do processo evolutivo. Nas gerações iniciais, os especialistas de diferentes classes interagem mais. Com a evolução do algoritmo, estas interações diminuem e mais tarde, dependendo da parametriação do algoritmo, elas podem ser eliminadas. Assim, no início do processo evolutivo há mais cooperação entre os especialistas de diferentes classes, o que favorece uma exploração mais ampla do espaço de busca. Com a eliminação da cooperação, favorece-se uma exploração mais local e detalhada deste espaço. Foram testados diferentes parâmetros do PG CPl utilizando o conjunto de dados iris do UCI Machine Learning Repository. Os resultados mostraram que a cooperação entre especialistas de diferentes classes ajudou na melhoria dos classificadores de classes mais difíceis de modelar. Além disso, que a evolução sem a interação entre as classes de diferentes especialidades beneficiou os classificadores quando eles já apresentam boa performance Uma combinação destes dois modos pode ser benéfica quando o algoritmo começa com classificadores que apresentam qualidades diferentes. O PG CPI também apresentou ótimos resultados para outros dois conjuntos de dados mais complexos o thyroid e o yeast, do mesmo repositório, alcançando acurácia similar aos melhores valores encontrados na literatura para outros modelos de CMC

The Application of Evolutionary Algorithms to the Classification of Emotion from Facial Expressions

Emotions are an integral part of human daily life as they can influence behaviour. A reliable emotion detection system may help people in varied things, such as social contact, health care and gaming experience. Emotions can often be identified by facial expressions, but this can be difficult to achieve reliably as people are different and a person can mask or supress an expression. Instead of analysis on static image, the computing of the motion of an expression’s occurrence plays more important role for these reasons. The work described in this thesis considers an automated and objective approach to recognition of facial expressions using extracted optical flow, which may be a reliable alternative to human interpretation. The Farneback’s fast estimation has been used for the dense optical flow extraction. Evolutionary algorithms, inspired by Darwinian evolution, have been shown to perform well on complex,nonlinear datasets and are considered for the basis of this automated approach. Specifically, Cartesian Genetic Programming (CGP) is implemented, which can find computer programme that approaches user-defined tasks by the evolution of solutions, and modified to work as a classifier for the analysis of extracted flow data. Its performance compared with Support Vector Machine (SVM), which has been widely used in expression recognition problem, on a range of pre-recorded facial expressions obtained from two separate databases (MMI and FG-NET). CGP was shown flexible to optimise in the experiments: the imbalanced data classification problem is sharply reduced by applying an Area under Curve (AUC) based fitness function. Results presented suggest that CGP is capable to achieve better performance than SVM. An automatic expression recognition system has also been implemented based on the method described in the thesis. The future work is to propose investigation of an ensemble classifier implementing both CGP and SVM

Evolutionary computing techniques to aid the acquisition and analysis of nuclear magnetic resonance data

Evolutionary computation, including genetic algorithms and genetic programming have taken the ideas of evolution in biology and applied some of the characteristics to problem solving. The survival of the fittest paradigm allows a population of candidate solutions to be modified by sexual and asexual reproduction and mutation to come closer to solving the problem in question without the necessity of having prior knowledge of what a good solution looks like. The increasing importance of Nuclear Magnetic Resonance Spectroscopy in medicine has created a demand for automated data analysis for tissue classification and feature selection. The use of artificial intelligence techniques such as evolutionary computing can be used for such data analysis. This thesis applies the techniques of evolutionary computation to aid the collection and classification of Nuclear Magnetic Resonance spectroscopy data. The first section (chapters one and two) introduces Nuclear Magnetic Resonance spectroscopy and evolutionary computation and also contains a review of relevant literature. The second section focuses on classification. In the third chapter classification into two classes of brain tumours is undertaken. The fourth chapter expands this to classify tumours and tissues into more than two classes. Genetic Programming provided good solutions with relatively simple biochemical interpretation and was able to classify data into more than two classes at one time. The third section of the thesis concentrates on using evolutionary computation techniques to optimise data acquisition parameters directly from the Nuclear Magnetic Resonance hardware. Chapter five shows that Genetic Algorithms in particular are successful at suppressing signals from solvent while chapter six applies these techniques to find a way of enhancing the signals from metabolites important to the classification of brain tumours as found in chapter three. The final chapter draws conclusions as to the efficacy of evolutionary computation techniques applied to Nuclear Magnetic Resonance Spectroscop

Utilising restricted for-loops in genetic programming

Genetic programming is an approach that utilises the power of evolution to allow computers to evolve programs. While loops are natural components of most programming languages and appear in every reasonably-sized application, they are rarely used in genetic programming. The work is to investigate a number of restricted looping constructs to determine whether any significant benefits can be obtained in genetic programming. Possible benefits include: Solving problems which cannot be solved without loops, evolving smaller sized solutions which can be more easily understood by human programmers and solving existing problems quicker by using fewer evaluations. In this thesis, a number of explicit restricted loop formats were formulated and tested on the Santa Fe ant problem, a modified ant problem, a sorting problem, a visit-every-square problem and a difficult object classificat ion problem. The experimental results showed that these explicit loops can be successfully used in genetic programming. The evolutionary process can decide when, where and how to use them. Runs with these loops tended to generate smaller sized solutions in fewer evaluations. Solutions with loops were found to some problems that could not be solved without loops. The results and analysis of this thesis have established that there are significant benefits in using loops in genetic programming. Restricted loops can avoid the difficulties of evolving consistent programs and the infinite iterations problem. Researchers and other users of genetic programming should not be afraid of loops

Genetic Programming for Classification with Unbalanced Data

In classification,machine learning algorithms can suffer a performance bias when data sets are unbalanced. Binary data sets are unbalanced when one class is represented by only a small number of training examples (called the minority class), while the other class makes up the rest (majority class). In this scenario, the induced classifiers typically have high accuracy on the majority class but poor accuracy on the minority class. As the minority class typically represents the main class-of-interest in many real-world problems, accurately classifying examples from this class can be at least as important as, and in some cases more important than, accurately classifying examples from the majority class. Genetic Programming (GP) is a promising machine learning technique based on the principles of Darwinian evolution to automatically evolve computer programs to solve problems. While GP has shown much success in evolving reliable and accurate classifiers for typical classification tasks with balanced data, GP, like many other learning algorithms, can evolve biased classifiers when data is unbalanced. This is because traditional training criteria such as the overall success rate in the fitness function in GP, can be influenced by the larger number of examples from the majority class. This thesis proposes a GP approach to classification with unbalanced data. The goal is to develop new internal cost-adjustment techniques in GP to improve classification performances on both the minority class and the majority class. By focusing on internal cost-adjustment within GP rather than the traditional databalancing techniques, the unbalanced data can be used directly or "as is" in the learning process. This removes any dependence on a sampling algorithm to first artificially re-balance the input data prior to the learning process. This thesis shows that by developing a number of new methods in GP, genetic program classifiers with good classification ability on the minority and the majority classes can be evolved. This thesis evaluates these methods on a range of binary benchmark classification tasks with unbalanced data. This thesis demonstrates that unlike tasks with multiple balanced classes where some dynamic (non-static) classification strategies perform significantly better than the simple static classification strategy, either a static or dynamic strategy shows no significant difference in the performance of evolved GP classifiers on these binary tasks. For this reason, the rest of the thesis uses this static classification strategy. This thesis proposes several new fitness functions in GP to perform cost adjustment between the minority and the majority classes, allowing the unbalanced data sets to be used directly in the learning process without sampling. Using the Area under the Receiver Operating Characteristics (ROC) curve (also known as the AUC) to measure how well a classifier performs on the minority and majority classes, these new fitness functions find genetic program classifiers with high AUC on the tasks on both classes, and with fast GP training times. These GP methods outperform two popular learning algorithms, namely, Naive Bayes and Support Vector Machines on the tasks, particularly when the level of class imbalance is large, where both algorithms show biased classification performances. This thesis also proposes a multi-objective GP (MOGP) approach which treats the accuracies of the minority and majority classes separately in the learning process. The MOGP approach evolves a good set of trade-off solutions (a Pareto front) in a single run that perform as well as, and in some cases better than, multiple runs of canonical single-objective GP (SGP). In SGP, individual genetic program solutions capture the performance trade-off between the two objectives (minority and majority class accuracy) using an ROC curve; whereas in MOGP, this requirement is delegated to multiple genetic program solutions along the Pareto front. This thesis also shows how multiple Pareto front classifiers can be combined into an ensemble where individual members vote on the class label. Two ensemble diversity measures are developed in the fitness functions which treat the diversity on both the minority and the majority classes as equally important; otherwise, these measures risk being biased toward the majority class. The evolved ensembles outperform their individual members on the tasks due to good cooperation between members. This thesis further improves the ensemble performances by developing a GP approach to ensemble selection, to quickly find small groups of individuals that cooperate very well together in the ensemble. The pruned ensembles use much fewer individuals to achieve performances that are as good as larger (unpruned) ensembles, particularly on tasks with high levels of class imbalance, thereby reducing the total time to evaluate the ensemble