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10 July 2009 Content-based butterfly image retrieval based on keyblock distribution
Wei Song, Cheng Cai, Xiang Qin, Yu Meng, Huan Hao
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Proceedings Volume 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering; 74890Q (2009) https://doi.org/10.1117/12.836877
Event: International Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2009), 2009, Zhangjiajie, China
Abstract
In the agricultural research area, the study about butterflies is very important. However, there is hardly any content-based butterfly image retrieval system. The text-based image retrieval system is not objective enough, and could not provide the characteristics of image content. Conventionally, the RGB color histogram-based image retrieval can't provide spatial features of images, and is easily affected by the pixel distribution, which is unable to represent the comprehensive characteristics of images. In this paper, we proposed a new butterfly image retrieval algorithm based on keyblock distribution. The keyblock-based image retrieval algorithm is a generalization of the technology in computer image retrieval area which is very advanced and useful. Our proposed butterfly image keyblock distribution extraction contains three procedures: first, a codebook with specific length is estimated by employing the vector quantization technique; second, the original butterfly image is divided into non-overlapping blocks; third, each block of butterfly image is encoded with the index number of codebook. From the keyblocks, we can extract both the color distribution information and the local spatial information of butterfly image. In the performance evaluation, experimental results show that in our retrieval system, average recall (AR) and average normalized modified retrieval rank (ANMRR) achieved 0.74 and 0.3291, respectively.
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Wei Song, Cheng Cai, Xiang Qin, Yu Meng, and Huan Hao "Content-based butterfly image retrieval based on keyblock distribution", Proc. SPIE 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering, 74890Q (10 July 2009); https://doi.org/10.1117/12.836877
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KEYWORDS
Image retrieval
RGB color model
Feature extraction
Databases
Computer programming
Agriculture
Autoregressive models
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