Multiple level visual semantic fusion method for image re-ranking

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• 作者：Shuhan Qi ; Fanglin Wang ; Xuan Wang ; Yue Guan ; Jia Wei ; Jian Guan
• 关键词：Image retrieval ; Re ; ranking ; Multiple feature fusion
• 刊名：Multimedia Systems
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：23
• 期：1
• 页码：155-167
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems; Computer Communication Networks; Operating Systems; Data Storage Representation; Data Encryption; Computer Graphics;
• 出版者：Springer Berlin Heidelberg
• ISSN：1432-1882
• 卷排序：23

文摘

Mid-level semantic attributes have obtained some success in image retrieval and re-ranking. However, due to the semantic gap between the low-level feature and intermediate semantic concept, information loss is considerable in the process of converting the low-level feature to semantic concept. To tackle this problem, we tried to bridge the semantic gap by looking for the complementary of different mid-level features. In this paper, a framework is proposed to improve image re-ranking by fusing multiple mid-level features together. The framework contains three mid-level features (DCNN-ImageNet attributes, Fisher vector, sparse coding spatial pyramid matching) and a semi-supervised multigraph-based model that combines these features together. In addition, our framework can be easily extended to utilize arbitrary number of features for image re-ranking. The experiments are conducted on the a-Pascal dataset, and our approach that fuses different features together is able to boost performance of image re-ranking efficiently.