Support vector machine active learning for image retrieval

Citations Over TimeTop 1% of 2001 papers

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.

Related Papers

• → Evaluating group-based relevance feedback for content-based image retrieval(2004)26 cited
• → Semantic image retrieval using relevance feedback and reinforcement learning algorithm(2010)3 cited
• → Context Window Based Co-Occurrence Approach for Improving Feedback Based Query Expansion in Information Retrieval(2018)1 cited
• → Query-by-sketch image retrieval using relevance feedback(2005)4 cited
• Information retrieval method based on data fusion and relevance feedback(2008)