摘要
图像相似度算法在图像识别、图像搜素引擎等研究领域具有重要意义。针对传统的灰度颜色直方图算法无法准确地描述各种颜色在图像中的分布情况的问题,提出了一种改进的图像相似度算法。它融合图像的纹理特征,利用灰度共生矩阵来提取图像像素在图像各个位置的特征信息。实验表明,这种融合图像纹理特征的方法不仅保留了灰度颜色直方图算法执行效率高的特点,而且弥补了颜色直方图算法的不足,进而提高了算法的准确性。在实际的应用场景中,可以通过调整两种算法的权值,来进一步提高算法的准确性。
Image similarity algorithm has important significance in image recognition, image search engines and other research areas. Traditional gray color histogram algorithm can not accurately describe the distribution of each color in the image. To address this issue, an improved image similarity algorithm was proposed. It fuses texture feature of image,and extracts image pixels- feature information at various positions on the image by using gray level matrix. Experimental results show that the method of fusing image texture features not only retains the characteristics of efficient implementation of gray color histogram algorithm, but also can improve the accuracy of the algorithm. In practical application scenarios, we can adjust the weights of two kinds of algorithm to further improve the accuracy of the algorithm.
出处
《计算机科学》
CSCD
北大核心
2016年第6期72-76,共5页
Computer Science
基金
国家自然科学基金(51179146)
中央高校基本科研业务费专项基金(2014-VII-027)
湖北省科技支撑计划(2015BAA120)
湖北省科技支撑计划(2015BCE068)资助
关键词
图像相似度
灰度级
颜色直方图
纹理特征
应用场景
Image similarity, Gray-level, Color histogram, Textural feature, Application scenarios
