The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Constructing models for content-based image retrieval
Open accessAbstract
This paper presents a new method for constructing models from a set of positive and negative sample images; the method requires no manual extraction of significant objects or features. Our model representation is based on two layers. The first one consists of "generic" descriptors which represent sets of similar rotational invariant feature vectors. Rotation invariance allows to group similar, but rotated patterns and makes the method robust to model deformations. The second layer is the joint probability on the frequencies of the "generic" descriptors over neighborhoods. This probability is multi-modal and is represented by a set of "spatial-frequency" clusters. It adds a statistical spatial constraint which is rotationally invariant. Our two-layer representation is novel; it allows to efficiently capture "texture-like" visual structure. The selection of distinctive structure determines characteristic model features (common to the positive and rare in the negative examples) and increases the performance of the model. Models are retrieved and localized using a probabilistic score. Experimental results for "textured" animals and faces show a very good performance for retrieval as well as localization.
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Cites
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
Feature detection with automatic scale selection
Lindeberg, T. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008045108935 · Cited by 2,541
Cited by
A sparse texture representation using local affine regions
Lazebnik, S., Schmid, C., & Ponce, J. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.151 · Cited by 1,123
Self-citationBerg, T. L., Berg, A. C., Edwards, J., Maire, M., White, R., Teh, Y.-W., Learned-Miller, E., & Forsyth, D. A. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315253 · Cited by 339
Dimitrov, A., & Golparvar-Fard, M. (2014) · Advanced Engineering Informatics · doi:10.1016/j.aei.2013.11.002 · Cited by 261
van Tulder, G., & de Bruijne, M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2526687 · Cited by 142
Real-Time Scale Selection in Hybrid Multi-scale Representations
Lindeberg, T., & Bretzner, L. (2003) · Lecture Notes in Computer Science · doi:10.1007/3-540-44935-3_11 · Cited by 85
Metadata
- Authors
- Schmid, C.
- Venue
- Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001
- Publisher
- IEEE Comput. Soc
- Published
- 2005-08-25
- Volume
- 2
- Pages
- II-39-II-45
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2152942156
- Cited by
- 367
- References
- 2
- FWCI
- 14.07
- Type
- conference paper
- Language
- en
- Open access
- green
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