论文信息 - Two-Level Content-Based Endoscope Image Retrieval

Two-Level Content-Based Endoscope Image Retrieval

Based on the analysis of endoscope image, in this paper, a new color quantification method is proposed to extract improved CCV and the V component shape invariant moment achieving image feature base. Inspiring from general information searching, the two-level content-based endoscope image retrieval is represented using the improved CCV and V component shape invariant moment guaranteeing the first retrieval recall. Experiments prove the efficiency of these methods.

[1] J. M. Zachary,et al. An Information Theoretic Approach to Content Based Image Retrieval. , 2000 .

[2] Ming-Kuei Hu,et al. Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[3] Sun Jun-ding. Spatial feature extraction and image retrieval based on entropy , 2006 .

[4] Faouzi Kossentini,et al. Reversible integer-to-integer wavelet transforms for image compression: performance evaluation and analysis , 2000, IEEE Trans. Image Process..

[5] Xiaoying Tai,et al. An Improved Approach Based on FCM Using Feature Fusion for Medical Image Retrieval , 2007, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007).

[6] YE Yin-lan. Image Retrieval Based on Color and Shape Feature , 2008 .

[7] Jing Huang,et al. Image indexing using color correlograms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8] Michael J. Swain,et al. Color indexing , 1991, International Journal of Computer Vision.

[9] Stéphane Mallat,et al. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[10] Jing Huang,et al. Color-Spatial Image Indexing and Applications , 1998 .

[11] Antoine Geissbühler,et al. A Review of Content{Based Image Retrieval Systems in Medical Applications { Clinical Bene(cid:12)ts and Future Directions , 2022 .