Learning with both unlabeled data and query logs for image search

• Learning with unlabeled images and query logs within unified framework.
• Using local scaling technique to calculate the affinity matrix for manifold ranking.
• Employing soft labels to make algorithm more robust to erroneous query logs.
• Developed a hybrid active learning solution with low computational cost.

One of the challenges in image search is to learn with few labeled examples. Existing solutions mainly focus on leveraging either unlabeled data or query logs to address this issue, but little is known in taking both into account. This work presents a novel learning scheme that exploits both unlabeled data and query logs through a unified Manifold Ranking (MR) framework. In particular, we propose a local scaling technique to facilitate MR by self-tuning the scale parameter, and a soft label propagation strategy to enhance the robustness of MR against erroneous query logs. Further, within the proposed MR framework, a hybrid active learning method is developed, which is effective and efficient to select the informative and representative unlabeled examples, so as to maximally reduce users’ labeling effort. An empirical study shows that the proposed scheme is significantly more effective than the state-of-the-art approaches.

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