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中文摘要：
基于支持向量机（Support Vector Machine,SVM）理论的相关反馈技术是可有效提高图像检索性能的重要手段之一.然而,大多数SVM反馈算法普遍受到小样本问题的制约.本文综合了集成学习、半监督学习和主动学习三种方法的技术特点,提出一种混合学习框架下的SVM反馈算法.该算法在Boosting迭代过程中使用了未标记图像,以增加个体SVM之间的差异,从而获得高效的集成学习模型.同时,高效的集成学习模型更有利于寻找富有信息（most-informative）图像,从而也提高了用户主动反馈的效率.实验结果及对比分析表明,混合学习策略可有效改进相关反馈的性能.
英文摘要：
Relevance feedback plays an important role for enhancing content-based image retrieval（CBIR）.Among various methods,support vector machine（SVM） based relevance feedback technique has drawn substantial research attention.However,most SVMfeedback approaches are challenged by the small example issue.This paper presents a SVM-feedback scheme within the hybrid learning framework that integrates the merits of ensemble learning,semi-supervised learning and active learning in order to achieve strong generalization.Concretely,in each round of boosting iterations,unlabeled images are exploited to augment the diversity among component SVM learners,and thus a powerful ensemble learning model is constructed.Conversely,the enhanced ensemble learning model is helpful to identify the most informative images which are used for active feedback.Experimental results,including comparison analysis,show that the hybrid learning framework is efficient to improve relevance feedback performance.
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