CMVF:A Novel Dimension Reduction Scheme for Efficient Indexing in A Large Image Database
Summary: CMVF combines PCA with nonlinear neural networks to hybridly reduce composite image features for scalable indexing. It yields compact, discriminative vectors that fuse perceptual cues and support adding features to boost distance-based indexing. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Jialie Shen (University of New South Wales)
- 2. Anne H.H. Ngu (Southwest Texas State University)
- 3. John Shepherd (University of New South Wales)
- 4. Du Q. Huynh (University of Western Australia)
- 5. Quan Z. Sheng (University of New South Wales)
BibTeX Citation
@inproceedings{shen_sigmod03,
title = {{CMVF:A Novel Dimension Reduction Scheme for Efficient Indexing in A Large Image Database}},
author = {Shen, Jialie and Ngu, Anne H.H. and Shepherd, John and Huynh, Du Q. and Sheng, Quan Z.},
series = {{SIGMOD} '03},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/872757.872842},
url = {https://dl.acm.org/doi/10.1145/872757.872842},
year = {2003}
}
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