Efficient Processing of Nearest Neighbor Queries in Parallel Multimedia Databases
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
An efficient key point quantization algorithm for large scale image retrieval
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Video sequence querying using clustering of objects' appearance models
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
An environment for video content indexing and retrieval base don visual features
WebMedia '09 Proceedings of the XV Brazilian Symposium on Multimedia and the Web
Visual memes in social media: tracking real-world news in YouTube videos
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Multimedia Applications and Security in MapReduce: Opportunities and Challenges
Concurrency and Computation: Practice & Experience
Journal of Visual Communication and Image Representation
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The proliferation of the web and digital photography have made large scale image collections containing billions of images a reality. Image collections on this scale make performing even the most common and simple computer vision, image processing, and machine learning tasks non-trivial. An example is nearest neighbor search, which not only serves as a fundamental subproblem in many more sophisticated algorithms, but also has direct applications, such as image retrieval and image clustering. In this paper, we address the nearest neighbor problem as the first step towards scalable image processing. We describe a scalable version of an approximate nearest neighbor search algorithm and discuss how it can be used to find near duplicates among over a billion images.