Please use this identifier to cite or link to this item: https://doi.org/10.1145/2393347.2396473
Title: Attribute feedback
Authors: Zhang, H.
Zha, Z.-J. 
Bian, J.
Gao, Y.
Luan, H. 
Chua, T.-S. 
Keywords: attribute feedback
image search
relevance feedback
Issue Date: 2012
Source: Zhang, H.,Zha, Z.-J.,Bian, J.,Gao, Y.,Luan, H.,Chua, T.-S. (2012). Attribute feedback. MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia : 1339-1340. ScholarBank@NUS Repository. https://doi.org/10.1145/2393347.2396473
Abstract: This demonstration presents a new interactive Content Based Image Retrieval (CBIR) system, termed Attribute Feedback (AF). Unlike traditional relevance feedback purely founded on low-level features, AF system shapes user's search intents more precisely and quickly by collecting feedbacks on intermediate-level semantic attribute. At each interaction iteration, the AF system first determines the most informative binary attributes for feedbacks and then augments the binary attribute feedbacks by a new type of attributes, "affinity attributes", each of which is learnt offline to describe the distance/similarity between user's envisioned image(s) and a retrieved image with respect to the corresponding affinity attribute. Based on the feedbacks on binary and affinity attributes, the images in corpus are further re-ranked towards better fitting user's search intents. The experimental results on two real-world image datasets have demonstrated the superiority of the AF system over other state-of-the-art relevance feedback based CBIR approaches. © 2012 Authors.
Source Title: MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia
URI: http://scholarbank.nus.edu.sg/handle/10635/41832
ISBN: 9781450310895
DOI: 10.1145/2393347.2396473
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