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A novel shape-based non-redundant local binary pattern descriptor for object detection

pattern RECOGNITION 2013年第5期46卷 1485-1500页

作者： Duc Thanh Nguyen Ogunbona, Philip O. Li, Wanqing Univ Wollongong Sch Comp Sci & Software Engn Wollongong NSW 2522 Australia

Motivated by the discriminative ability of shape information and local patterns in object recognition, this paper proposes a window-based object descriptor that integrates both cues. In particular, contour templates representing object shape are used to derive a set of so-called key points at which local appearance features are extracted. These key points are located using an improved template matching method that utilises both spatial and orientation information in a simple and effective way. At each of the extracted key points, a new local appearance feature, namely non-redundant local binary pattern (NR-LBP), is computed. An object descriptor is formed by concatenating the NR-LBP features from all key points to encode the shape as well as the appearance of the object. The proposed descriptor was extensively tested in the task of detecting humans from static images on the commonly used MIT and INRIA datasets. The experimental results have shown that the proposed descriptor can effectively describe non-rigid objects with high articulation and improve the detection rate compared to other state-of-the-art object descriptors. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Object description Object detection non-redundant local binary pattern Generalised distance transform Template matching

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Robust Approaches for Multi-label Face Classification

Robust Approaches for Multi-label Face Classification

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International Conference on Digital Image Computing - Techniques and Applications (DICTA)

作者： Mohammed, Ahmed Abdulateef Sajjanhar, Atul Deakin Univ Sch Informat Technol Geelong Vic 3216 Australia

ISBN: (纸本)9781509028962

Texture analysis is widely used to extract facial attributes which are used for face classification. In this paper, novel approaches for texture analysis are proposed to improve the performance of face classification. The proposed methods are derived by applying local binary pattern based approaches on polar raster sampled face images. The proposed methods are evaluated by performing classification of face images using multilabel classification models. Experimental results show that the proposed methods outperform local binary pattern method and its variants.

关键词： texture analysis face classification local binary pattern Compound local binary pattern non-redundant local binary pattern multi-label classification

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Food image classification using local appearance and global structural information

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NEUROCOMPUTING 2014年 140卷 242-251页

作者： Duc Thanh Nguyen Zong, Zhimin Ogunbona, Philip O. Probst, Yasmine Li, Wanqing Univ Wollongong Sch Comp Sci & Software Engn Wollongong NSW 2522 Australia Univ Wollongong Sch Hlth Sci Wollongong NSW 2522 Australia

This paper proposes food image classification methods exploiting both local appearance and global structural information of food objects. The contribution of the paper is threefold. First, non-redundant local binary pattern (NRLBP) is used to describe the local appearance information of food objects. Second, the structural information of food objects is represented by the spatial relationship between interest points and encoded using a shape context descriptor formed from those interest points. Third, we propose two methods of integrating appearance and structural information for the description and classification of food images. We evaluated the proposed methods on two datasets. Experimental results verified that the combination of local appearance and structural features can improve classification performance. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Food image classification local binary pattern non-redundant local binary pattern Shape context

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Inter-occlusion reasoning for human detection based on variational mean field

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NEUROCOMPUTING 2013年 110卷 51-61页

作者： Duc Thanh Nguyen Li, Wanqing Ogunbona, Philip O. Univ Wollongong Sch Comp Sci & Software Engn Wollongong NSW 2522 Australia

Detecting multiple humans in crowded scenes is challenging because the humans are often partially or even totally occluded by each other. In this paper, we propose a novel algorithm for partial inter-occlusion reasoning in human detection based on variational mean field theory. The proposed algorithm can be integrated with various part-based human detectors using different types of features, object representations, and classifiers. The algorithm takes as the input an initial set of possible human objects (hypotheses) detected using a part-based human detector. Each hypothesis is decomposed into a number of parts and the occlusion status of each part is inferred by the proposed algorithm. Specifically, initial detections (hypotheses) with spatial layout information are represented in a graphical model and the inference is formulated as an estimation of the marginal probability of the observed data in a Bayesian network. The variational mean field theory is employed as an effective estimation technique. The proposed method was evaluated on popular datasets including CAVIAR, iLIDS, and INRIA. Experimental results have shown that the proposed algorithm is not only able to detect humans under severe occlusion but also enhance the detection performance when there is no occlusion. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Human detection Occlusion reasoning non-redundant local binary pattern Mean field method

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