基于改进特征的图像检索方法研究
Research of Image Retrieval Method Based on Improved Feature

针对在图像检索过程中，传统单一特征不能较好反映图像的颜色分布和内容细节等相关信息，降低了图像检索性能的问题，提出一种基于改进颜色和纹理综合特征的图像检索方法。根据HSV颜色模型区域均值，利用改进关联权值模型，获取颜色均值特征向量；基于Haar小波进行图像分解变换。在图像的低频分量中，根据低频特征结构模型，获取低频纹理特征向量；通过Canberra距离求取图像相似度。实验结果表明：方法在Corel-1000和Corel-5000标准图库中进行测试，准确率和检索率等性能参数得到了相应提高。
In the process of image retrieval, the traditional single feature can't reflect the distribution and details of image color and content, which have some adverse influence on the performance of image retrieval. This paper presents an image retrieval method based on improved color and texture feature. According to the mean of the HSV color model region, algorithm obtains the mean eigenvectors of the color feature by using the improved correlation weight model. The image decomposition transformation is obtained through the Haar wavelet. In the low-frequency component of the image decomposition, the low-frequency texture feature vector is obtained according to the low-frequency feature structure model. The similarity of image is calculated by the Canberra distance. Experimental results show that:the methods of retrieval are tested in Corel-1000 and Corel-5000 standard gallery, which accuracy rate and retrieval rate have been improved accordingly