A Combing Top-Down and Bottom-Up Discriminative Dictionaries Learning for Non-specific Object Detection

Yurui XIE  Qingbo WU  Bing LUO  Chao HUANG  Liangzhi TANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E97-D   No.5   pp.1367-1370
Publication Date: 2014/05/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E97.D.1367
Type of Manuscript: LETTER
Category: Pattern Recognition
dictionary learning,  object detection,  

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In this letter, we exploit a new framework for detecting the non-specific object via combing the top-down and bottom-up cues. Specifically, a novel supervised discriminative dictionaries learning method is proposed to learn the coupled dictionaries for the object and non-object feature spaces in terms of the top-down cue. Different from previous dictionary learning methods, the new data reconstruction residual terms of coupled feature spaces, the sparsity penalty measures on the representations and an inconsistent regularizer for the learned dictionaries are all incorporated in a unitized objective function. Then we derive an iterative algorithm to alternatively optimize all the variables efficiently. Considering the bottom-up cue, the proposed discriminative dictionaries learning is then integrated with an unsupervised dictionary learning to capture the objectness windows in an image. Experimental results show that the non-specific object detection problem can be effectively solved by the proposed dictionary leaning framework and outperforms some established methods.