Fast discovery of association rules
Advances in knowledge discovery and data mining
Indexing large metric spaces for similarity search queries
ACM Transactions on Database Systems (TODS)
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
CLUE: cluster-based retrieval of images by unsupervised learning
IEEE Transactions on Image Processing
Diversifying the image retrieval results
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Image clustering using local discriminant models and global integration
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
BPM'11 Proceedings of the 9th international conference on Business process management
Semi-supervised image classification for automatic construction of a health image library
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Improving the image retrieval results via topic coverage graph
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
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A two-scale image retrieval system is developed to provide efficient search in large-scale databases as well as flexibility for users to incorporate ubjective preferences during retrieval. A new clustering method is developed for images each characterized by a varying number of weighted feature vectors. Furthermore, significant meta-information is mined within every cluster. A scanning mode of retrieval is created using cluster centers, which serve as a low scale version of a database in contrast to original images. In particular, users are presented with representative images of highly ranked clusters along with prominent meta-information. This retrieval approach enables users to quickly examine a large and diverse portion of a database surrounding a query and to learn about hidden connections between visual patterns and non-imagery types of data. The clusters formed also facilitate fast search in the case of individual image-based retrieval by filtering out images whose cluster centers are far from the query. The two-scale retrieval system has been implemented on a fine art painting database. Advantages of the system have been demonstrated by quantitative evaluation of the retrieval performance.