Robust visual reranking via sparsity and ranking constraints
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Find you wherever you are: geographic location and environment context-based pedestrian detection
Proceedings of the ACM multimedia 2012 workshop on Geotagging and its applications in multimedia
Multimedia search reranking: A literature survey
ACM Computing Surveys (CSUR)
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Most existing approaches to visual search reranking predominantly focus on mining information only from the initial ranking order on the basis of pseudo-relevance feedback. However, the initial ranking order cannot always provide enough cues for reranking by itself due to an unsatisfying visual search performance. This letter presents a novel approach to visual search reranking by selecting typical examples to build the reranking model. Observing that typical examples are mostly clearly visible, fill the majority of the visual documents or appear in one of several common poses, by using these examples informed classifiers would generally be more robust to noisy testing cases that may include occlusions, illumination changes or other factors. We first define the typicality on the basis of data distribution, and then theoretically formalize the example selection as an optimization problem on the basis of the example typicality and propose a close-form solution. Based on the selected examples, we build the reranking model by using a support vector machine. Empirically, we conduct extensive experiments on a real-world image set and a benchmark video set, and shows significant and consistent improvements over the state-of-the-art works.