遗传反馈的多特征图像检索

Relevance feedback image retrieval based on multiple features

基于内容的图像检索是随着数字多媒体技术的发展和普及而新兴的一门信息检索技术。针对当前该领域存在的对图像描述不准确、查询精度低以及反馈次数较多的问题,提出一种基于遗传反馈的图像检索算法。该算法以遗传算法和相关反馈为基础,利用多特征进行检索,避免在利用单一特征进行检索时所出现的不同图像具有相同单一特征(颜色、纹理和形状等)的问题,对图像进行多特征描述可以从多个角度对图像进行定义,大大减少了不同图像却具有相同特征的概率。与现有的算法相比,其具有自动调整图像特征权重、较低反馈次数和较高查询精度的特性。实验结果表明,该算法对于旋转、平移和尺度变化具有较强的鲁棒性,同时具有减少反馈次数和较高查询精度的性能。

Content-based image retrieval （CBIR） is a new information retrieval technology along with development of the digital multimedia technology. The paper proposes relevance feedback techniques and genetic algorithm for image retrieval based on multiple features in allusion to the problem of inaccurate description, low query precision and high Frequency of feedback, which can avoid causing the problem of different images with the same single feature （ color, texture and shape）. Compared with the existing algorithms, the proposed algorithm can automatically adjust the image feature weights. The experimental results show that the proposed algorithm is robust for rotation, translation and scale changes strongly. In addition, the proposed algorithm has higher query precision and lower frequency of feedback simultaneously.