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
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive objects retrieval with efficient boosting
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Logo retrieval with a contrario visual query expansion
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Correlation-based burstiness for logo retrieval
Proceedings of the 20th ACM international conference on Multimedia
Rapid object search engine for contextual advertisement
Proceedings of the 20th ACM international conference on Multimedia
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Searching for small objects (e.g., logos) in images is a critical yet challenging problem. It becomes more difficult when target objects differ significantly from the query object due to changes in scale, viewpoint or style, not to mention partial occlusion or cluttered backgrounds. With the goal to retrieve and accurately locate the small object in the images, we formulate the object search as the problem of finding subimages with the largest mutual information toward the query object. Each image is characterized by a collection of local features. Instead of only using the query object for matching, we propose a discriminative matching using both positive and negative queries to obtain the mutual information score. The user can verify the retrieved subimages and improve the search results incrementally. Our experiments on a challenging logo database of 10,000 images highlight the effectiveness of this approach.