Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Video search reranking via information bottleneck principle
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Robust visual reranking via sparsity and ranking constraints
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Bridging the Semantic Gap Between Image Contents and Tags
IEEE Transactions on Multimedia
Aggregating Local Image Descriptors into Compact Codes
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this work, we target at solving the Bing challenge provided by Microsoft. The task is to design an effective and efficient measurement of query terms in describing the images (image-query pairs) crawled from the web. We observe that the provided image-query pairs (e.g., text-based image retrieval results) are usually related to their surrounding text; however, the relationship between image content seems to be ignored. Hence, we attempt to integrate the visual information for better ranking results. In addition, we found that plenty of query terms are related to people (e.g., celebrity) and user might have similar queries (click logs) in the search engine. Therefore, in this work, we propose a relevance association by investigating the effectiveness of different auxiliary contextual cues (i.e., face, click logs, visual similarity). Experimental results show that the proposed method can have 16% relative improvement compared to the original ranking results. Especially, for people-related queries, we can further have 45.7% relative improvement.