Sciweavers

Share
MM
2004
ACM

Multimodal concept-dependent active learning for image retrieval

9 years 4 months ago
Multimodal concept-dependent active learning for image retrieval
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 complexity. In this work, we first characterize a concept’s complexity using three measures: hitrate, isolation and diversity. We then propose a multimodal learning approach that uses images’ semantic labels to guide a concept-dependent, active-learning 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 300K-image dataset shows that concept-dependent learning is highly effective for image-retrieval accuracy. Categories and Subject Descriptors I.5.1 [Pattern Recognition]: Models—stat...
Kingshy Goh, Edward Y. Chang, Wei-Cheng Lai
Added 30 Jun 2010
Updated 30 Jun 2010
Type Conference
Year 2004
Where MM
Authors Kingshy Goh, Edward Y. Chang, Wei-Cheng Lai
Comments (0)
books