ACM Multimedia 2013 best paper award goes to….

ACM Multimedia 2013 best paper award goes to Attribute-augmented Semantic Hierarchy for Image Retrieval

This paper presents a novel Attribute-augmented Semantic Hierarchy (A² SH) and demonstrates its effectiveness in bridging both the semantic and intention gaps in Content-based Image Retrieval (CBIR). A² SH organizes the semantic concepts into multiple semantic levels and augments each concept with a set of related attributes, which describe the multiple facets of the concept and act as the intermediate bridge connecting the concept and low-level visual content. A hierarchical semantic similarity function is learnt to characterize the semantic similarities among images for retrieval. To better capture user search intent, a hybrid feedback mechanism is developed, which collects hybrid feedbacks on attributes and images. These feedbacks are then used to refine the search results based on A² SH. We develop a content-based image retrieval system based on the proposed A² SH. We conduct extensive experiments on a large-scale data set of over one million Web images. Experimental results show that the proposed A² SH can characterize the semantic affinities among images accurately and can shape user search intent precisely and quickly, leading to more accurate search results as compared to state-of-the-art CBIR solutions.

http://dl.acm.org/citation.cfm?id=2502093