Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Late fusion of heterogeneous methods for multimedia image retrieval
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Learning tag relevance by neighbor voting for social image retrieval
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Visual query suggestion: Towards capturing user intent in internet image search
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Properties of optimally weighted data fusion in CBMIR
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Content-based tag processing for Internet social images
Multimedia Tools and Applications
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This paper documents a comprehensive empirical study of the effects of heterogeneous information combination on large scale social image search. Our goal is to investigate how various kinds of information source can contribute the improvement of the retrieval effectiveness. In particular, a linear combination has been applied to merge search results from search module based on textual and visual features. Also, we propose different weighting schemes to integrate different kinds of query evidences in a nonlinear way. A series of experiments have been conducted using two large scale social image collections. Empirical results suggest that the system based on textual features yields much more effective and reliable results comparing to one using visual information. Further, the combination of two information sources can consistently enhance the final accuracy.