Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Descriptive visual words and visual phrases for image applications
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
Interactive visual object search through mutual information maximization
Proceedings of the international conference on Multimedia
Randomized visual phrases for object search
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Visual object search, with the goal to find and locate the target object in large image or video collections, is of great interest for many applications and hence has received intensive attentions in recent years. In this demo, we present a spatial context-aware large-scale visual object search system, which is robust to cluttered backgrounds and can well handle scale variations of the objects. Different from the traditional image retrieval systems only matching individual points or fixed-scale spatial contexts, the proposed system considers spatial contexts of varying sizes and shapes, in the form of randomized spatial partition (RSP), and hence provides more accurate search results. Moreover, compared to the computational expensive RANSAC algorithm used in the state-of-the-art retrieval systems, the RSP framework lends our system to easy parallelization and significant speedup for object localization. Consequently, our system works accurately and efficiently. In addition, an Android application has been developed for mobile tasks, by which the user can take a photo of the object he/she wants and then search the same products and their selling information.