A new bag-of-words model using multi-cue integration for image retrieval. (2016)

• Record Type: Journal Article
• Authors: Wu, Junfeng; Li, Zhiyang; Qu, Wenyu; Li, Yuanyuan
• Is Part Of: International journal of computational science and engineering. Volume 13:Number 1(2016)
• Page Start: 80
• Page End: 86
• Publication Date: 2016
• Subjects: bag-of-words -- BoW -- SIFT descriptor -- saliency maps -- colour histograms -- multi-cue integration -- image retrieval -- colour features -- shape features; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

Bag-of-words-based (BoW) image retrieval has been the most popular method in the image retrieval field. In traditional BoW scheme, the whole scheme for image retrieval utilises only one feature. The images are represented by the visual vocabulary consisting of a single feature. But in many cases, one feature could not describe the images well, some false matches often occur in traditional BoW scheme. To solve the problem, the paper proposes a multi-cue framework to integrate colour and shape features in order to improve the retrieval performance. To demonstrate the effectiveness of our method, we evaluate our new framework on the Simplicity dataset and Stanford dataset. The experimental results illustrate the proposed method could significantly improve the retrieval performance.