IEEE Trans Image Process - Task-dependent visual-codebook compression.

Tópicos

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Resumo

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 a few bins are nonzero and distributed sparsely. Furthermore, most existing codebooks are built based solely on the visual statistics of local descriptors, without considering the supervise labels coming from the subsequent recognition or classification tasks. In this paper, we propose a task-dependent codebook compression framework to handle the above two problems. First, we propose to learn a compression function to map an originally high-dimensional codebook into a compact codebook while maintaining its visual discriminability. This is achieved by a codeword sparse coding scheme with Lasso regression, which minimizes the descriptor distortions of training images after codebook compression. Second, we propose to adapt our codebook compression to the subsequent recognition or classification tasks. This is achieved by introducing a label constraint kernel (LCK) into our compression loss function. In particular, our LCK can model heterogeneous kinds of supervision, i.e., (partial) category labels, correlative semantic annotations, and image query logs. We validated our codebook compression in three computer vision tasks: 1) object recognition in PASCAL Visual Object Class 07; 2) near-duplicate image retrieval in UKBench; and 3) web image search in a collection of 0.5 million Flickr photographs. Our compressed codebook has shown superior performances over several state-of-the-art supervised and unsupervised codebooks.

Resumo Limpo

visual codebook serv fundament compon mani stateoftheart comput vision system exist codebook built base quantiz local featur descriptor extract train imag subsequ imag repres highdimension bagofword histogram high redund imag descript lack effici storag retriev bin nonzero distribut spars furthermor exist codebook built base sole visual statist local descriptor without consid supervis label come subsequ recognit classif task paper propos taskdepend codebook compress framework handl two problem first propos learn compress function map origin highdimension codebook compact codebook maintain visual discrimin achiev codeword spars code scheme lasso regress minim descriptor distort train imag codebook compress second propos adapt codebook compress subsequ recognit classif task achiev introduc label constraint kernel lck compress loss function particular lck can model heterogen kind supervis ie partial categori label correl semant annot imag queri log valid codebook compress three comput vision task object recognit pascal visual object class neardupl imag retriev ukbench web imag search collect million flickr photograph compress codebook shown superior perform sever stateoftheart supervis unsupervis codebook

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