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http://dx.doi.org/10.3837/tiis.2016.02.018

A Salient Based Bag of Visual Word Model (SBBoVW): Improvements toward Difficult Object Recognition and Object Location in Image Retrieval  

Mansourian, Leila (University Putra Malaysia, Faculty of Computer Science and Information Technology, Department of Multimedia, UPM Serdang)
Abdullah, Muhamad Taufik (University Putra Malaysia, Faculty of Computer Science and Information Technology, Department of Multimedia, UPM Serdang)
Abdullah, Lilli Nurliyana (University Putra Malaysia, Faculty of Computer Science and Information Technology, Department of Multimedia, UPM Serdang)
Azman, Azreen (University Putra Malaysia, Faculty of Computer Science and Information Technology, Department of Multimedia, UPM Serdang)
Mustaffa, Mas Rina (University Putra Malaysia, Faculty of Computer Science and Information Technology, Department of Multimedia, UPM Serdang)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.10, no.2, 2016 , pp. 769-786 More about this Journal
Abstract
Object recognition and object location have always drawn much interest. Also, recently various computational models have been designed. One of the big issues in this domain is the lack of an appropriate model for extracting important part of the picture and estimating the object place in the same environments that caused low accuracy. To solve this problem, a new Salient Based Bag of Visual Word (SBBoVW) model for object recognition and object location estimation is presented. Contributions lied in the present study are two-fold. One is to introduce a new approach, which is a Salient Based Bag of Visual Word model (SBBoVW) to recognize difficult objects that have had low accuracy in previous methods. This method integrates SIFT features of the original and salient parts of pictures and fuses them together to generate better codebooks using bag of visual word method. The second contribution is to introduce a new algorithm for finding object place based on the salient map automatically. The performance evaluation on several data sets proves that the new approach outperforms other state-of-the-arts.
Keywords
saliency map; SIFT feature; Bag of Visual Words model (BoVW); image retrieval; object recognition; and object location;
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