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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chatterjee, K. Shu-Ching Chen |
| Copyright Year | 2006 |
| Description | Author affiliation: Sch. of Comput. & Inf. Sci., Florida Int. Univ., Miami, FL (Chatterjee, K.; Shu-Ching Chen) |
| Abstract | A novel indexing and access method, called affinity hybrid tree (AH-tree), is proposed to organize large image data sets efficiently and to support popular image access mechanisms like content-based image retrieval (CBIR) by embedding the high-level semantic image-relationship in the access mechanism as it is. AH-tree combines space-based and distance-based indexing techniques to form a hybrid structure which is efficient in terms of computational overhead and fairly accurate in producing query results close to human perception. Algorithms for similarity (range and k-nearest neighbor) queries are implemented. Results from elaborate experiments are reported which depict a low computational overhead in terms of the number of I/O and distance computations and a high relevance of query results. The proposed index structure solves the existing problems of introducing high-level image relationships in a retrieval mechanism without going through the pain of translating the content-similarity measurement into feature-level equivalence and yet maintaining an efficient structure to organize the large sets of images |
| Starting Page | 47 |
| Ending Page | 54 |
| File Size | 209472 |
| Page Count | 8 |
| File Format | |
| ISBN | 0769527469 |
| DOI | 10.1109/ISM.2006.21 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-12-11 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Indexing Information retrieval Image retrieval Content based retrieval Multimedia databases Multimedia systems Multidimensional systems Indexes Image databases Multimedia computing |
| Content Type | Text |
| Resource Type | Article |
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