Attribute feedback

Hanwang Zhang, Zheng-Jun Zha, Jingwen Bian, Yue Gao, Huan-Bo Luan, Tat-Seng Chua

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.