An Improved Relevance Feedback in CBIR
This work addresses incremental improvements in image retrieval systems for users in fields like multimedia search.
The paper tackles improving retrieval accuracy in Content-Based Image Retrieval (CBIR) by enhancing Relevance Feedback methods, including a novel approach to boost initial retrieval accuracy using feedback information.
Relevance Feedback in Content-Based Image Retrieval is a method where the feedback of the performance is being used to improve itself. Prior works use feature re-weighting and classification techniques as the Relevance Feedback methods. This paper shows a novel addition to the prior methods to further improve the retrieval accuracy. In addition to all of these, the paper also shows a novel idea to even improve the 0-th iteration retrieval accuracy from the information of Relevance Feedback.