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A visual codebook serves as a fundamental component in many state-of-the-art computer vision systems. Most existing codebooks are built based on quantizing local feature descriptors extracted from training images. Subsequently, each image is represented as a high-dimensional bag-of-words histogram. Such highly redundant image description lacks efficiency in both storage and retrieval, in which only...
When codeword frequency meets geographical location in landmark search applications, is it still discriminative for the search procedure. In this paper, we give a systematic investigation about how geographical location affects the effectiveness of codeword frequency. We explain why the standard IDF in the BoW models is less effective in location related search applications [11][12]. Consequently,...
This paper presents a framework to extract subset which only contains one person's video segments from a superfluous home video set. This is a semantic level multimedia application. We proposed a co-clustering method based on facial and body feature, and defined a new type of measurement to combine the two features more reasonable. 2D-PCA detector is used to extract facial feature, and K-Means algorithm...
Traditional background modeling and subtraction methods have a strong assumption that the scenes are of static structures with limited perturbation. These methods will perform poorly in dynamic scenes. In this paper, we present a solution to this problem. We first extend the local binary patterns from spatial domain to spatio-temporal domain, and present a new online dynamic texture extraction operator,...
Two main restrictions exist in state-of-the-art text detection algorithms: 1. Illumination variance; 2. Text-background contrast variance. This paper presents a robust text characterization approach based on local Haar binary pattern (LHBP) to address these problems. Based on LHBP, a coarse-to-fine detection framework is presented to precisely locate text lines in scene images. Firstly, threshold-restricted...
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