Please use this identifier to cite or link to this item:
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 13, 2017
checked on Dec 16, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.