Object recognition based on the Region of Interest and optimal Bag of Words model

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• 作者：Weisheng Li^a ; ^{liws@cqupt.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Peng Dong^a ; ^{clasdong@qq.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Bin Xiao^a ; ^{xiaobin@cqupt.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Lifang Zhou^b ; ^{zhoulf@cqupt.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Object recognition ; Region of Interest ; Bag of Words model ; k-means++ cluster ; Gaussian mixture models
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：8 January 2016
• 年：2016
• 卷：172
• 期：Complete
• 页码：271-280
• 全文大小：3180 K

文摘

Bag of Words (BoW) model has been widely used in conventional object recognition tasks. Different from the existing methods, this paper proposed a method for object recognition based on Region of Interest (ROI) and Optimal Bag of Words model. It includes the following steps: (1) ROI extraction in combination with the Shi–Tomasi corner and Itti saliency map; (2) The SIFT feature descriptors are detected and described on images of interest; (3) A visual codebook is generated through utilizing the Gaussian mixture models, which relies on the clustering results of k-means++; (4) The similarities between each visual word and corresponding local feature are computed by posterior pseudo probabilities discriminative to construct a visual word soft histogram for image representation; (5) The Support vector machine (SVM) is used to perform image classification and recognition. The experiments are performed on the MSRC 21-class database. The results show that the proposed method can be more accurately recognize images.