Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking

With efficient appearance learning models, Discriminative Correlation Filter (DCF) has been proven to be very successful in recent video object tracking benchmarks and competitions. However, the existing DCF paradigm suffers from two major issues, i.e., spatial boundary effect and temporal filter degradation. To mitigate these challenges, we propose a new DCF-based tracking method. The key innovations of the proposed method include adaptive spatial feature selection and temporal consistent constraints, with which the new tracker enables joint spatial-temporal filter learning in a lower dimensional discriminative manifold. More specifically, we apply structured spatial sparsity constraints to multi-channel filers. Consequently, the process of learning spatial filters can be approximated by the lasso regularisation. To encourage temporal consistency, the filter model is restricted to lie around its historical value and updated locally to preserve the global structure in the manifold. Last, a unified optimisation framework is proposed to jointly select temporal consistency preserving spatial features and learn discriminative filters with the augmented Lagrangian method. Qualitative and quantitative evaluations have been conducted on a number of well-known benchmarking datasets such as OTB2013, OTB50, OTB100, Temple-Colour, UAV123 and VOT2018. The experimental results demonstrate the superiority of the proposed method over the state-of-the-art approaches

Evolutionary computation-based feature selection for finding a stable set of features in high-dimensional data

Evolutionary Computation (EC) algorithms have proved to work well for feature selection because they are powerful search techniques and can produce multiple good solutions. However, they suffer from some limitations for real world applications. Firstly, ECs require high computation time as they evaluate many solutions at each iteration. Secondly, a classifier is usually used as their fitness function which causes the selected subset to perform well only on the utilised classifier (e.g. classifier-bias). Lastly, ECs, as stochastic search methods, return a different final subset in different runs which poses a problem for finding a stable set of features (e.g. stability issue). To address computation time and classifier-bias limitations, this thesis proposes a new two-stage selection approach called filter/filter in which two filter feature selection algorithms are combined. In the first stage, a ranking algorithm forms a reduced dataset by selecting the most informative features from the original dataset. In the second stage, the reduced dataset is fed to a novel EC algorithm to select final feature subset. This new EC algorithm is a Tabu search hybridised with an Asexual Genetic Algorithm called TAGA. TAGA benefits from new search components and solution representation which can effectively reduce computation time. To select a classifier-unbiased final subset, a statistical criterion is used as the fitness function which evaluates the subset independent of any classifier. Experiments show that the proposed filter/filter requires an acceptable computation time and selects more classifier-unbiased features compared to the state-of-the-arts. To find a stable set of features, a novel Generalisation Power Index (GPI) is proposed to analyse the generalisation power of final subsets of an EC in several runs. Generalisation power refers to performance capability of a subset over wide range of classifiers. Computation results confirm that GPI is able to find a stable set of features which achieves near optimal accuracy when used to train various classifiers. To ex amine the suitability of the proposed methods for real-world applications, the filter/filter approach and GPI are integrated to select a stable set of features for METABRIC breast cancer subtype classification problem. Experimental results show that this integration not only can address the limitations of ECs for a real-world biomedical feature selection problem but it performs better than alternatives methods

Evolutionary improvement of programs

Most applications of genetic programming (GP) involve the creation of an entirely new function, program or expression to solve a specific problem. In this paper, we propose a new approach that applies GP to improve existing software by optimizing its non-functional properties such as execution time, memory usage, or power consumption. In general, satisfying non-functional requirements is a difficult task and often achieved in part by optimizing compilers. However, modern compilers are in general not always able to produce semantically equivalent alternatives that optimize non-functional properties, even if such alternatives are known to exist: this is usually due to the limited local nature of such optimizations. In this paper, we discuss how best to combine and extend the existing evolutionary methods of GP, multiobjective optimization, and coevolution in order to improve existing software. Given as input the implementation of a function, we attempt to evolve a semantically equivalent version, in this case optimized to reduce execution time subject to a given probability distribution of inputs. We demonstrate that our framework is able to produce non-obvious optimizations that compilers are not yet able to generate on eight example functions. We employ a coevolved population of test cases to encourage the preservation of the function's semantics. We exploit the original program both through seeding of the population in order to focus the search, and as an oracle for testing purposes. As well as discussing the issues that arise when attempting to improve software, we employ rigorous experimental method to provide interesting and practical insights to suggest how to address these issues

Compilation of relations for the antisymmetric tensors defined by the Lie algebra cocycles of su(n)su(n)

This paper attempts to provide a comprehensive compilation of results, many new here, involving the invariant totally antisymmetric tensors (Omega tensors) which define the Lie algebra cohomology cocycles of $su(n)$, and that play an essential role in the optimal definition of Racah-Casimir operators of $su(n)$. Since the Omega tensors occur naturally within the algebra of totally antisymmetrised products of $\lambda$-matrices of $su(n)$, relations within this algebra are studied in detail, and then employed to provide a powerful means of deriving important Omega tensor/cocycle identities. The results include formulas for the squares of all the Omega tensors of $su(n)$. Various key derivations are given to illustrate the methods employed.Comment: Latex file (run thrice). Misprints corrected, Refs. updated. Published in IJMPA 16, 1377-1405 (2001