New human action recognition scheme with geometrical feature representation and invariant discretization for video surveillance

Human action recognition is an active research area in computer vision because of its immense application in the field of video surveillance, video retrieval, security systems, video indexing and human computer interaction. Action recognition is classified as the time varying feature data generated by human under different viewpoint that aims to build mapping between dynamic image information and semantic understanding. Although a great deal of progress has been made in recognition of human actions during last two decades, few proposed approaches in literature are reported. This leads to a need for much research works to be conducted in addressing on going challenges leading to developing more efficient approaches to solve human action recognition. Feature extraction is the main tasks in action recognition that represents the core of any action recognition procedure. The process of feature extraction involves transforming the input data that describe the shape of a segmented silhouette of a moving person into the set of represented features of action poses. In video surveillance, global moment invariant based on Geometrical Moment Invariant (GMI) is widely used in human action recognition. However, there are many drawbacks of GMI such that it lack of granular interpretation of the invariants relative to the shape. Consequently, the representation of features has not been standardized. Hence, this study proposes a new scheme of human action recognition (HAR) with geometrical moment invariants for feature extraction and supervised invariant discretization in identifying actions uniqueness in video sequencing. The proposed scheme is tested using IXMAS dataset in video sequence that has non rigid nature of human poses that resulting from drastic illumination changes, changing in pose and erratic motion patterns. The invarianceness of the proposed scheme is validated based on the intra-class and inter-class analysis. The result of the proposed scheme yields better performance in action recognition compared to the conventional scheme with an average of more than 99% accuracy while preserving the shape of the human actions in video images

A New Swarm-Based Framework for Handwritten Authorship Identification in Forensic Document Analysis

Feature selection has become the focus of research area for a long time due to immense consumption of high-dimensional data. Originally, the purpose of feature selection is to select the minimally sized subset of features class distribution which is as close as possible to original class distribution. However in this chapter, feature selection is used to obtain the unique individual significant features which are proven very important in handwriting analysis of Writer Identification domain. Writer Identification is one of the areas in pattern recognition that have created a center of attention by many researchers to work in due to the extensive exchange of paper documents. Its principal point is in forensics and biometric application as such the writing style can be used as bio-metric features for authenticating the identity of a writer. Handwriting style is a personal to individual and it is implicitly represented by unique individual significant features that are hidden in individual’s handwriting. These unique features can be used to identify the handwritten authorship accordingly. The use of feature selection as one of the important machine learning task is often disregarded in Writer Identification domain, with only a handful of studies implemented feature selection phase. The key concern in Writer Identification is in acquiring the features reflecting the author of handwriting. Thus, it is an open question whether the extracted features are optimal or near-optimal to identify the author. Therefore, feature extraction and selection of the unique individual significant features are very important in order to identify the writer, moreover to improve the classification accuracy. It relates to invarianceness of authorship where invarianceness between features for intra-class (same writer) is lower than inter-class (different writer). Many researches have been done to develop algorithms for extracting good features that can reflect the authorship with good performance. This chapter instead focuses on identifying the unique individual significant features of word shape by using feature selection method prior the identification task. In this chapter, feature selection is explored in order to find the most unique individual significant features which are the unique features of individual’s writing. This chapter focuses on the integration of Swarm Optimized and Computationally Inexpensive Floating Selection (SOCIFS) feature selection technique into the proposed hybrid of Writer Identification framework 386 S.F. Pratama et al. and feature selection framework, namely Cheap Computational Cost Class-Specific Swarm Sequential Selection (C4S4). Experiments conducted to proof the validity and feasibility of the proposed framework using dataset from IAM Database by comparing the proposed framework to the existing Writer Identification framework and various feature selection techniques and frameworks yield satisfactory results. The results show the proposed framework produces the best result with 99.35% classification accuracy. The promising outcomes are opening the gate to future explorations in Writer Identification domain specifically and other domains generally

Human action invarianceness for human action recognition

The uniqueness of the human action shape or silhouete can be used for the human action recognition. Acquiring the features of human silhouette to obtained the concept of human action invarianceness have led to an important research in video surveillance domain. This paper discusses the investigation of this concept by extracting individual human action features using integration moment invariant. Experiment result have shown that human action invarianceness are improved with better recognition accuracy. This has verified that the integration method of moment invariant is worth explored in recognition of human action in video surveillance