Support vector machine active learning for image retrieval

Support vector machine active learning for image retrieval (2001)

by Simon Tong

Citations:

Datum	Value	Source
TITLE	Support vector machine active learning for image retrieval	INFERENCE
AUTHOR NAME	Simon Tong	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science; Stanford University	SVM HeaderParse 0.2
AUTHOR ADDR	Sanford, CA 94305	SVM HeaderParse 0.2
ABSTRACT	Relevance feedback is often a critical component when designing image databases. With these databases it is difficult to specify queries directly and explicitly. Relevance feedback interactively determinines a user’s desired output or query concept by asking the user whether certain proposed images are relevant or not. For a relevance feedback algorithm to be effective, it must grasp a user’s query concept accurately and quickly, while also only asking the user to label a small number of images. We propose the use of a support vector machine active learning algorithm for conducting effective relevance feedback for image retrieval. The algorithm selects the most informative images to query a user and quickly learns a boundary that separates the images that satisfy the user’s query concept from the rest of the dataset. Experimental results show that our algorithm achieves significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.	SVM HeaderParse 0.2
YEAR	2001	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	107--118	INFERENCE
CITATIONS	33 found	ParsCit 1.0