CAJ | 학술논문

基于内容的图像检索是当前多媒体信息检索的热点之一。基于内容的图像检索技术是根据对图像内容（特征）的描述和提取，在图像库中找到具有指定内容（特征）的图像。本文对图像颜色特征和纹理特征的提取、相似性度量等基于内容的图像检索的关键技术进行了分析和研究，并在此基础上，提出了一个基于颜色特征和纹理特征的图像检索算法并验证了其有效性。该算法采用HSV颜色空间的直方图作为颜色特征向量，采用灰度共生矩阵的四个纹理特征：能量、熵、惯’性矩和相关性构成纹理特征向量，采用欧氏距离进行相似性度量。实验结果表明，该算法实现的系统具有良好的图像检索功能。
기우내용적도상검색시당전다매체신식검색적열점지일。기우내용적도상검색기술시근거대도상내용（특정）적묘술화제취，재도상고중조도구유지정내용（특정）적도상。본문대도상안색특정화문리특정적제취、상사성도량등기우내용적도상검색적관건기술진행료분석화연구，병재차기출상，제출료일개기우안색특정화문리특정적도상검색산법병험증료기유효성。해산법채용HSV안색공간적직방도작위안색특정향량，채용회도공생구진적사개문리특정：능량、적、관’성구화상관성구성문리특정향량，채용구씨거리진행상사성도량。실험결과표명，해산법실현적계통구유량호적도상검색공능。
Content-Based Image Retrieval （CBIR） is one of the most active hot spots in the current research field of multimedia retrieval. According to the description and extraction of visual content （feature） of the image, CBIR aims to find images that contain specified content （feature） in the image database. In this paper, several key technologies of CBIR such as the extraction of the color and texture features of the image, as well as the similarity measures are investigated. On the basis of the theory research, an image retrieval system based on color and texture features is designed, which uses Histogram based on H$V color space as color feature vector, uses four of the features of Co-occurrence Matrix to construct texture vector which are Energy, Entropy, InertiaQuadrature and Correlation, and uses Euclidean distance for similarity measure. Experiments results show that this CBIR system is efficient in image retrieval.