Active learning and its scalability for image retrieval

Author

Chang, Edward Y. ; Lai, Wei-Cheng

Author_Institution

California Univ., Santa Barbara, CA, USA

Volume

1

fYear

2004

fDate

27-30 June 2004

Firstpage

73

Abstract

Active learning has been shown to be a viable tool for learning complex, subjective query concepts with a small number of training instances. In this work, we compare four active-learning algorithms and study the best sample-selection strategies. We also discuss two scalability issues of active learning: scalability in dataset size, and scalability in concept complexity. To address these challenges, we suggest future directions that research might take.

Keywords

image retrieval; learning (artificial intelligence); query formulation; support vector machines; SVM; active learning; angle-diversity algorithm; concept complexity scalability; concept diversity; concept isolation; concept scarcity; dataset size scalability; error-reduction algorithm; image retrieval; kernel methods; sample-selection strategies; speculative active learning; subjective query concepts; support vectors; target query-concept formulation; training instances; Binary trees; Feedback; Image retrieval; Kernel; Performance analysis; Performance evaluation; Scalability; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Multimedia and Expo, 2004. ICME '04. 2004 IEEE International Conference on

Print_ISBN

0-7803-8603-5

Type

conf

DOI

10.1109/ICME.2004.1394128

Filename

1394128