文献类型：期刊文献

英文题名：Image region annotation based on segmentation and semantic correlation analysis

作者：Zhang, Jing[1];Mu, Yakun[1];Feng, Shengwei[1];Li, Kehuang[2];Yuan, Yubo[1];Lee, Chin-Hui[2]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA

年份：2018

卷号：12

期号：8

起止页码：1331

外文期刊名：IET IMAGE PROCESSING

收录：;EI(收录号：20183305694346);WOS:【SCI-EXPANDED(收录号:WOS:000441141600005)】；

基金：Part of this research was supported by the National Nature Science Foundation of China (grant 61402174).

语种：英文

外文关键词：Image analysis - Syntactics - Image annotation - Textures - Image enhancement - Image segmentation

摘要：The authors propose an image region annotation framework by exploring syntactic and semantic correlations among segmented regions in an image. A texture-enhanced image segmentation JSEG algorithm is first used to improve the pixel consistency in a segmented image region. Next, each region is represented by a set of image codewords, also known as visual alphabets, with each of them used to characterise certain low-level image features. A visual lexicon, with its vocabulary items defined as either a codeword or a co-occurrence of multiple alphabets, is formed and used to model middle-level semantic concepts. The concept classification models are trained by a maximal figure-of-merit algorithm with a collection of training images with multiple correlations, including spatial, syntactic and semantic relationship, between regions and their corresponding concepts. In addition, a region-semantic correlation model constructed with latent semantic analysis is used to correct the potentially wrong annotations by analysing the relationship between image region positions and labels. When evaluated on the Corel 5K dataset, the proposed image region annotation framework achieves accurate results on image region concept tagging as well as whole image based annotations.