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中文摘要：
学习不同模态的多媒体数据在底层特征上的潜在关系，在降维得到的特征子空间中通过基于相似度传递的优化算法对图像和音频的聚类质量进行修正．相关反馈过程中设计了3种主动学习策略用以计算用户标注样本周围未标注样本的条件概率，从而在反馈样本有限的情况下提高跨媒体检索效率．实验结果表明该方法准确度量跨媒体的相关性，有效实现图像和音频数据之间的相互检索．
英文摘要：
The latent correlation between low-level features of different modalities is studied, and an optimizing algorithm is proposed to improve cluster quality of both image and audio datasets in the feature subspace. To speed up the convergence of query process, three active learning strategies in relevance feedback are incorporated. Thus, the condition probability of unlabeled samples around labeled examples is calculated. Experimental results show that overall cross-media retrieval results are encouraging, and the mutual retrieval between image and audio data can be effectively realized by the proposed algorithm.
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