基于内容图像检索中的顺序回归问题

Ordinal Regression in Content-Based Image Retrieval

相关反馈技术是基于内容图像检索研究的一个重要组成部分.近年来,人们对相关反馈算法开展了许多研究工作,并提出了多种算法.目前,多数的相关反馈算法都是基于二值的相关度量——相关或不相关.为了更好地辨别用户的需要和偏好,就需要考虑相关性在程度上的差异而采用更精细的度量尺度.探讨了支持多级相关度量的相关反馈问题,指出相关反馈问题可以看成是一个顺序回归问题,并讨论了它的特点和损失函数.基于持向量学习算法,提出了一种新的相关反馈方案.由于传统的检索性能度量(比如查准率和查全率)不适合多级相关度量的情况,采用了一种建立在图像间偏好关系上的检索性能度量.在现实世界图像数据库上的实验结果验证了所提出相关反馈方法的有效性.

Relevance feedback, as a key component of content-based image retrieval, attracted much research attention in the past few years, and a lot of algorithms are proposed. Most current relevance feedback algorithms use dichotomy relevance measurement-relevance or non-relevance. To better identify the user's needs and preferences, a refined relevance scale should be used to represent the degree of relevance. Relevance feedback with multilevel relevance measurement is studied. Relevance feedback is considered as an ordinal regression problem, and its properties and loss function are discussed. A new relevance feedback scheme is proposed based on a support vector learning algorithm for ordinal regression. Since the traditional retrieval performance measures, such as precision and recall, are not appropriate for retrieval with multilevel relevance measurement, a new performance measure is introduced, which is based on the preference relation between images, The proposed relevance feedback approach is tested on a real-world image database, and promising results are achieved.