Landmark Image Retrieval by Jointing Feature Refinement and Multimodal Classifier Learning

• Published in: IEEE transactions on cybernetics Vol. 48; no. 6; pp. 1682 - 1695
• Main Authors: Zhang, Xiaoming, Wang, Senzhang, Li, Zhoujun, Ma, Shuai
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Classifiers; Correlation; Feature extraction; Image classification; Image color analysis; image geo-tagging; Image management; Image recognition; Image retrieval; Jointing; landmark retrieval; Landmarks; Similarity; Urban areas; Visual observation; Visualization
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2017.2712798

Landmark retrieval is to return a set of images with their landmarks similar to those of the query images. Existing studies on landmark retrieval focus on exploiting the geometries of landmarks for visual similarity matches. However, the visual content of social images is of large diversity in many landmarks, and also some images share common patterns over different landmarks. On the other side, it has been observed that social images usually contain multimodal contents, i.e., visual content and text tags, and each landmark has the unique characteristic of both visual content and text content. Therefore, the approaches based on similarity matching may not be effective in this environment. In this paper, we investigate whether the geographical correlation among the visual content and the text content could be exploited for landmark retrieval. In particular, we propose an effective multimodal landmark classification paradigm to leverage the multimodal contents of social image for landmark retrieval, which integrates feature refinement and landmark classifier with multimodal contents by a joint model. The geo-tagged images are automatically labeled for classifier learning. Visual features are refined based on low rank matrix recovery, and multimodal classification combined with group sparse is learned from the automatically labeled images. Finally, candidate images are ranked by combining classification result and semantic consistence measuring between the visual content and text content. Experiments on real-world datasets demonstrate the superiority of the proposed approach as compared to existing methods.