Distance-based indexing for high-dimensional metric spaces
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Towards effective indexing for very large video sequence database
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
ACM Transactions on Database Systems (TODS)
Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Multi-probe LSH: efficient indexing for high-dimensional similarity search
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
International Journal of Approximate Reasoning
Efficient and accurate nearest neighbor and closest pair search in high-dimensional space
ACM Transactions on Database Systems (TODS)
Self-taught hashing for fast similarity search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
BRIEF: binary robust independent elementary features
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Effective data co-reduction for multimedia similarity search
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
HashFile: An efficient index structure for multimedia data
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Multiple feature hashing for real-time large scale near-duplicate video retrieval
MM '11 Proceedings of the 19th ACM international conference on Multimedia
LDAHash: Improved Matching with Smaller Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterative quantization: A procrustean approach to learning binary codes
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Locality-sensitive hashing scheme based on dynamic collision counting
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Fast and Accurate Fingerprint Indexing Based on Ridge Orientation and Frequency
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalized Biased Discriminant Analysis for Content-Based Image Retrieval
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Manhattan hashing for large-scale image retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Inter-media hashing for large-scale retrieval from heterogeneous data sources
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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With the rapid development of the Internet and multimedia technologies over the last decade, a huge amount of data has become available, from text corpus, to collections of online images and videos. Cheap storage cost and modern database technologies have made it possible to accumulate large-scale datasets. However, the ever-growing sizes of the datasets make it harder to search useful information from such data. A fundamental computational primitive for dealing with massive multimedia datasets is the similarity search problem. Multimedia similarity search aims to preprocess a database so that given a query object, one can quickly find its similar objects in the database. Searching similar objects from a large dataset in high-dimensional spaces is at the heart of many multimedia applications, such as near-duplicate retrieval, multimedia tagging, recommendation, and so on. Driven by its significance, lots of efforts have been made on this topic. The goal of my research is to design efficient hashing methods for large-scale multimedia search. In this paper, we first present the general framework for multimedia similarity search and discuss the latest improvements and progresses in the field. Then we describe the contributions we have made to effectively and efficiently search similar multimedia objects from large-scale databases. Finally, we discuss the future work and draw a conclusion.