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
An efficient parts-based near-duplicate and sub-image retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Size matters: exhaustive geometric verification for image retrieval accepted for ECCV 2012
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Mobile product image search by automatic query object extraction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Hough Pyramid Matching: Speeded-Up Geometry Re-ranking for Large Scale Image Retrieval
International Journal of Computer Vision
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The so-called bag-of-features (BoF) representation for images is by now well-established in the context of large scale image and video retrieval. The BoF framework typically ranks database image according to a metric on the global histograms of the query and database images, respectively. Ranking based on global histograms has the advantage of being scalable with respect to the number of database images, but at the cost of reduced retrieval precision when the object of interest is small. Additionally, computationally intensive post-processing (such as RANSAC) is typically required to locate the object of interest in the retrieved images. To address these shortcomings, we propose a generalization of the global BoF framework to support scalable local matching. Specifically, we propose an efficient and accurate algorithm to accomplish local histogram matching and object localization simultaneously. The generalization is to represent each database image as a family of histograms that depend functionally on a bounding rectangle. Integral with the image retrieval process, we identify bounding rectangles whose histograms optimize query relevance, and rank the images accordingly. Through this localization scheme, we impose a weak spatial consistency constraint with low computational overhead. We validate our approach on two public image retrieval benchmarks: the University of Kentucky data set and the Oxford Building data set. Experiments show that our approach significantly improves on BoF-based retrieval, without requiring computationally expensive post-processing.