Combined feature descriptor for Content based Image Retrieval

S. Selvarajah, S. Kodithuwakku
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引用次数: 16

Abstract

Content based Image Retrieval (CBIR) allows automatically extracting target images according to objective visual contents of the image itself. Representation of visual features and similarity match are important issues in CBIR. Colour and texture features are important properties in CBIR systems. In this paper, a combined feature descriptor for CBIR is proposed to enhance the retrieval performance for CBIR. This method is developed by exploiting the wavelets and colour histogram moments. First, Haar wavelet is used to decompose colour images into wavelet coefficients. Then image feature extraction and similarity matching are performed by means of histogram moments. Ten categories of colour images are used to test the proposed technique. Experiment results show significant improvement of retrieval efficiency as compared to that of histogram moments as well as 2D Discrete Wavelet Transform.
基于内容的图像检索的组合特征描述符
基于内容的图像检索(CBIR)可以根据图像本身的客观视觉内容自动提取目标图像。视觉特征的表示和相似度匹配是图像识别中的重要问题。颜色和纹理特征是CBIR系统的重要特性。本文提出了一种组合特征描述符,以提高组合特征描述符的检索性能。该方法是利用小波和颜色直方图矩来实现的。首先利用哈尔小波对彩色图像进行小波系数分解;然后利用直方图矩进行图像特征提取和相似度匹配。使用十类彩色图像来测试所提出的技术。实验结果表明,与直方图矩和二维离散小波变换相比,该方法的检索效率有显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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