基于支持向量机和用户反馈的图像检索算法

针对当前图像检索算法存在精度低、 实时性差等不足, 为了获得更理想的图像检索结果, 提出一种基于支持向量机和用户反馈机制的图像检索算法. 首先采集大量图像, 提取图像检索的相关特征, 建立图像检索特征库； 然后采用支持向量机计算待检索图像特征与图像检索库特征之间的相似度, 确定图像类别, 实现图像的初步检索； 最后引入用户反馈机制对图像的初步检测结果进行精细比对, 并与经典图像检索算法进行对比实验. 实验结果表明, 该方法的图像检索精度超过90%, 图像检索误差远小于经典图像检索算法, 提高了图像检索效率.

Image Retrieval Algorithm Based on User Feedback and Support Vector Machine

Aiming at the shortcomings of the current image retrieval algorithm, such as low accuracy, poor real time performance, in order to obtain more ideal image retrieval results, the author proposed an image retrieval algorithm based on support vector machine and user feedback mechanism. Firstly, the author collected large images, extracted the relevant features of image retrieval, and established image retrieval feature library. Secondly, the author used support vector machine to calculate the similarity between the features of the image to be retrieved and the features of image retrieval library, determined the image category, and realized the preliminary image retrieval. Finally, the author introduced the user feedback mechanism to refine the preliminary image detection results, and compared with the classical image retrieval algorithm. The experimental results show that the image retrieval accuracy of the proposed method is more than 90%, the image retrieval error is much smaller than that of the classical image retrieval algorithm, and improves the image retrieval efficiency.