How to complete performance graphs in content-based image retrieval: add generality and normalize scope
The performance of a content-based image retrieval (CBIR) system, presented in the form of precision-recall or precision-scope graphs, offers an incomplete overview of the system under study: the influence of the irrelevant items (embedding) is obscured. We propose a comprehensive and well-normalize...
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence 2005-02, Vol.27 (2), p.245-251 |
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| container_title | IEEE transactions on pattern analysis and machine intelligence |
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| creator | Huijsmans, D.P. Sebe, N. |
| description | The performance of a content-based image retrieval (CBIR) system, presented in the form of precision-recall or precision-scope graphs, offers an incomplete overview of the system under study: the influence of the irrelevant items (embedding) is obscured. We propose a comprehensive and well-normalized description of the ranking performance compared to the performance of an ideal retrieval system defined by ground-truth for a large number of predefined queries. We advocate normalization with respect to relevant class size and restriction to specific normalized scope values (the number of retrieved items). We also propose new three and two-dimensional performance graphs for total recall studies in a range of embeddings. |
| doi_str_mv | 10.1109/TPAMI.2005.30 |
| format | Article |
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| subjects | Algorithms Applied sciences Artificial Intelligence Computer science control theory systems Computer Simulation Content based retrieval content-based image retrieval Databases, Factual Exact sciences and technology Graphs Humans Image databases Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image retrieval Index Terms- Multimedia information systems Indexing Information retrieval Information Storage and Retrieval - methods Information systems Intelligence NIST Pattern analysis Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry performance evaluation Queries Recall Reproducibility of Results Retrieval Sensitivity and Specificity Size measurement Subtraction Technique Testing Two dimensional |
| title | How to complete performance graphs in content-based image retrieval: add generality and normalize scope |
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