Similarity measure is a critical component in image retrieval systems, and learning similarity measure from the relevance feedback has become a promising way to enhance retrieval performance. Existing approaches mainly focus on learning the visual similarity measure from online feedbacks or constructing the semantic similarity measure depended on historical feedbacks log. However, there is still a big room to elevate the retrieval performance, because few works take the relationship between the visual similarity and the semantic similarity into account. This paper proposes the collaborative learning similarity measure, CoSim, which focuses on the collaborative learning between the visual content of images and the hidden semantic in log. Concretely, the semantic similarity is first learned from log data and serves as prior knowledge. Then, the visual similarity is learned from a mixture of labeled and unlabeled images. In particular, unlabeled images are exploited for the relevant and irrelevant classes in different ways. Finally, the collaborative learning similarity is produced by integrating the visual similarity and the semantic similarity in a nonlinear way. An empirical study shows that the proposed CoSim is significantly more effective than some existing approaches.

Support vector machine (SVM) based active learning technique plays a key role to alleviate the burden of labeling in relevance feedback. However, most SVM-based active learning algorithms are challenged by the small example problem and the asymmetric distribution problem. This paper proposes a novel active learning scheme that deals with SVM ensemble under the semi-supervised setting to address the fist problem. For the second problem, a bias-ensemble mechanism is developed to guide the classification model to pay more attention on the positive examples than the negative ones. An empirical study shows that the proposed scheme is significantly more effective than some existing approaches.