基于弱监督E2LSH和显著图加权的目标分类方法

doi:10.11999/JEIT150337

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Abstract

The most popular approach in object classification is based on the bag of visual-words model. However, there are several fundamental problems that restricts the performance of this method, such as low time efficiency, the synonym and polysemy of visual words, and the lack of spatial information between visual words. In view of this, an object classification method based on weakly supervised Exact Euclidean Locality Sensitive Hashing (E2LSH) and saliency map weighting is proposed. Firstly, E2LSH is employed to generate a group of visual dictionary by clustering SIFT features of the training dataset, and the selecting process of hash functions is effectively supervised inspired by the random forest ideas to reduce the randomcity of E2LSH. Secondly, Graph-Based Visual Saliency (GBVS) algorithm is applied to detect the saliency map of different images and visual words are weighted according to the saliency prior. Finally, saliency map weighted visual language model is carried out to accomplish object classification. Experimental results on datasets of Caltech-256 and Pascal 2007 indicate that the distinguishability of objects is effectively improved and the proposed method is superior to the state- of-the-art object classification methods.

Key words： Object classification Bag of visual words model Exact Euclidean Locality Sensitive Hashing (E2LSH) Visual saliency map Visual language model

Received: 23 March 2015 Published: 17 November 2015

PACS:

TP391

Fund:

The National Natural Science Foundation of China (60872142, 61301232)

Corresponding Authors: ZHAO Yongwei E-mail: zhaoyongwei369@163.com

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	ZHAO Yongwei
	LI Bicheng
	KE Shengcai

Cite this article:

ZHAO Yongwei,LI Bicheng,KE Shengcai. Object Classification Method Based on Weakly Supervised E2LSH and Saliency Map Weighting[J]. JEIT, 2016, 38(1): 38-46.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT150337 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I1/38

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