King-Shy Goh
ABSTRACT
It has been established that active learning is effective for learning complex, subjective query concepts for image retrieval. However, active learning has been applied in a concept independent way, (i.e., the kernel-parameters and the sampling strategy are identically chosen) for learning query concepts of differing <i>complexity</i>. In this work, we first characterize a concept's complexity using three measures: <i>hit-rate</i>, <i>isolation</i> and <i>diversity</i>. We then propose a multimodal learning approach that uses images' semantic labels to guide a <i>concept-dependent</i>, <i>active-learning</i> process. Based on the complexity of a concept, we make intelligent adjustments to the sampling strategy and the sampling pool from which images are to be selected and labeled, to improve concept learnability. Our empirical study on a $300$K-image dataset shows that concept-dependent learning is highly effective for image-retrieval accuracy.
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Index Terms
- Multimodal concept-dependent active learning for image retrieval
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