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Object tracking is a challenging problem in computer vision as many performance affecting factors need to be considered in a robust algorithm. We propose a framework to consolidate Integral Channel Features (ICF) to represent targets' appearance by embedding global and patch based approaches which offer feature strength and accuracy to the target template. The use of ICF expedites the extraction of...
The paper suggests a novel approach for visual key-points description. Our approach is basically integrate the descriptors of local features (e.g. SURF (Speeded Up Robust Features)) and HOG (Histograms of Oriented Gradients) using patch-based integration to enhance the distinctness between image objects. It was tested in the context of image categorization using bag of features model. The experimental...
Existing tracking approaches are design-varied. Several features have been proposed to describe low, mid and high level cues and used to drive frame to frame tracking correspondences under mainly two strategies: purely matching and discriminative matching. However, despite the enormous amount of approaches proposed, single object tracking is still an unresolved task. In this paper we explore a, to...
Many multimedia applications can benefit from recognizing image content. It requires a robust and discriminative representation of objects, especially in the situation of only a few training samples available. In this paper, we present a new approach to integrate the advantages of bag-of-words model and part-based model for image recognition. Each image is encoded as a hierarchical word image (HWI),...
Local binary pattern histogram (LBPH) is one of the popular and excellent image texture descriptor. However, conventional LBPH lacks of the description of spatial structure information. This paper proposes an extension of LBPH called Markov chain local binary patterns (MCLBP) to alleviate this limitation. We apply MCLBP to the task of TRECVID video concept detection. Experimental results demonstrate...
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