文献类型：期刊文献

英文题名：Representation of image content based on RoI-BoW

作者：Zhang, Jing[1,2]; Li, Da[1]; Zhao, Yaxin[1]; Chen, Zhihua[1]; Yuan, Yubo[1]

机构：[1] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] State Key Lab. for Novel Software Technology, Nanjing University, Nanjing, 210093, China

年份：2015

卷号：26

起止页码：37

外文期刊名：Journal of Visual Communication and Image Representation

收录：EI(收录号：20144900279568)

语种：英文

外文关键词：Content based retrieval - Image representation - Gabor filters

摘要：Representation of image content is an important part of image annotation and retrieval, and it has become a hot issue in computer vision. As an efficient and accurate image content representation model, bag-of-words (BoW) has attracted more attention in recent years. After segmentation, BoW treats all of the image regions equally. In fact, some regions of image are more important than others in image retrieval, such as salient object or region of interest. In this paper, a novel region of interest based bag-of-words model (RoI-BoW) for image representation is proposed. At first, the difference of Gaussian (DoG) is adopted to find key points in an image and generates different size grid as RoI to construct visual words by the BoW model. Furthermore, we analyze the influence of different size segmentation on image content representation by content based image retrieval. Experiments on Corel 5K verify the effectiveness of RoI-BoW on image content representation, and prove that RoI-BoW outperforms the BoW model significantly. Moreover, amounts of experiments illustrate the influence of different size segmentation on image representation based on the Bow model and RoI-BoW model respectively. This work is helpful to choose appropriate grid size in different situations when representing image content, and meaningful to image classification and retrieval. ? 2014 Elsevier Inc. All rights reserved.