Evaluation of image similarity by histogram intersection

Colour is the most widely used attribute in image retrieval and object recognition. A technique known as histogram intersection has been widely studied and is considered to be effective for color‐image indexing. The key issue of this algorithm is the selection of an appropriate color space and optimal quantization of the selected color space. The goal of this article is to measure the model performance in predicting human judgment in similarity measurement for various images, to explore the capability of the model with a wide set of color spaces, and to find the optimal quantization of the selected color spaces. Six color spaces and twelve quantization levels are involved in evaluating the performance of histogram intersection. The categorical judgment and rank order experiments were conducted to measure image similarity. The CIELAB color space was found to perform at least as good as or better than the other color spaces tested, and the ability to predict image similarity increased with the number of bins used in the histograms, for up to 512 bins (8 per channel). With more than 512 bins, further improvement was negligible for the image datasets used in this study. © 2005 Wiley Periodicals, Inc. Col Res Appl, 30, 265–274, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20122     

高可靠性烧结金属丝网防爆元件的应用研究

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Perturbation analysis and control of two-class stochastic fluid models for communication networks

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微波仪器远程自动测试系统的设计与实现

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利用马铃薯淀粉下脚液生产饮料工艺的研究

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各向加权异性有限元法在不可压粘性流动计算中的应用

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β—BBO晶体的电光性能

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