IEEE Trans Image Process - Robust image analysis with sparse representation on quantized visual features.

Tópicos

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Resumo

Recent techniques based on sparse representation (SR) have demonstrated promising performance in high-level visual recognition, exemplified by the highly accurate face recognition under occlusion and other sparse corruptions. Most research in this area has focused on classification algorithms using raw image pixels, and very few have been proposed to utilize the quantized visual features, such as the popular bag-of-words feature abstraction. In such cases, besides the inherent quantization errors, ambiguity associated with visual word assignment and misdetection of feature points, due to factors such as visual occlusions and noises, constitutes the major cause of dense corruptions of the quantized representation. The dense corruptions can jeopardize the decision process by distorting the patterns of the sparse reconstruction coefficients. In this paper, we aim to eliminate the corruptions and achieve robust image analysis with SR. Toward this goal, we introduce two transfer processes (ambiguity transfer and mis-detection transfer) to account for the two major sources of corruption as discussed. By reasonably assuming the rarity of the two kinds of distortion processes, we augment the original SR-based reconstruction objective with l(0) norm regularization on the transfer terms to encourage sparsity and, hence, discourage dense distortion/transfer. Computationally, we relax the nonconvex l(0) norm optimization into a convex l(1) norm optimization problem, and employ the accelerated proximal gradient method to optimize the convergence provable updating procedure. Extensive experiments on four benchmark datasets, Caltech-101, Caltech-256, Corel-5k, and CMU pose, illumination, and expression, manifest the necessity of removing the quantization corruptions and the various advantages of the proposed framework.

Resumo Limpo

recent techniqu base spars represent sr demonstr promis perform highlevel visual recognit exemplifi high accur face recognit occlus spars corrupt research area focus classif algorithm use raw imag pixel propos util quantiz visual featur popular bagofword featur abstract case besid inher quantiz error ambigu associ visual word assign misdetect featur point due factor visual occlus nois constitut major caus dens corrupt quantiz represent dens corrupt can jeopard decis process distort pattern spars reconstruct coeffici paper aim elimin corrupt achiev robust imag analysi sr toward goal introduc two transfer process ambigu transfer misdetect transfer account two major sourc corrupt discuss reason assum rariti two kind distort process augment origin srbase reconstruct object l norm regular transfer term encourag sparsiti henc discourag dens distortiontransf comput relax nonconvex l norm optim convex l norm optim problem employ acceler proxim gradient method optim converg provabl updat procedur extens experi four benchmark dataset caltech caltech corelk cmu pose illumin express manifest necess remov quantiz corrupt various advantag propos framework

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