文献类型：期刊文献

英文题名：Adaptive all-season image tag ranking by saliency-driven image pre-classification

作者：Feng, Songhe[1,4];Lang, Congyan[1];Liu, Hongzhe[2];Huang, Xiankai[3]

第一作者：Feng, Songhe

通讯作者：Feng, SH[1]

机构：[1]Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing, Peoples R China;[2]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China;[3]Beijing Union Univ, Tourism Inst, Beijing, Peoples R China;[4]Beijing Jiaotong Univ, Beijing Key Lab Traff Data Anal & Min, Beijing, Peoples R China

第一机构：Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing, Peoples R China

通讯机构：[1]corresponding author), Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing, Peoples R China.

年份：2013

卷号：24

期号：7

起止页码：1031-1039

外文期刊名：JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION

收录：;EI(收录号：20133116552629);Scopus(收录号：2-s2.0-84880617296);WOS:【SCI-EXPANDED(收录号:WOS:000324848700027)】；

基金：This work is supported by National Nature Science Foundation of China (61100142, 61272352, 61271369), the Fundamental Research Funds for the Central Universities (2011JBM218, 2012JBM040), the Doctoral Fund of Ministry of Education of China (20110009120005) and the Science Foundation of Beijing Jiaotong University (2012RC008).

语种：英文

外文关键词：Adaptive tag ranking; Visual attention model; Sparse representation; Multi-instance learning; Tag saliency ranking; Tag relevance ranking; Image pre-classification; Gray histogram descriptor

摘要：Social image tag ranking has emerged as an important research topic due to its application on web image search. This paper presents an adaptive all-season tag ranking algorithm which can handle the images with and without distinct object(s) using different tag ranking strategies. Firstly, based on saliency map derived from the visual attention model, a linear SVM is trained to pre-classify an image as attentive or non-attentive category by using the gray histogram descriptor on the corresponding saliency map. Then, an image with distinct object is processed by the tag saliency ranking algorithm emphasizing distinct object, which combines image saliency map with sparse representation based multi-instance learning algorithm. On the other hand, an image without distinct object can be processed by the tag relevance ranking algorithm via the sparse representation based neighbor-voting strategy. Such adaptive all-season tag ranking strategy can be regarded as taking full advantage of existing tag ranking paradigms. Experiments conducted on well-known image data sets demonstrate the effectiveness of the proposed framework. (C) 2013 Elsevier Inc. All rights reserved.