Improving image annotation via ranking‐oriented neighbor search and learning‐based keyword propagation. (6th May 2014)

• Record Type: Journal Article
• Authors: Cui, Chaoran; Ma, Jun; Lian, Tao; Chen, Zhumin; Wang, Shuaiqiang
• Is Part Of: Journal of the Association for Information Science and Technology. Volume 66:Number 1(2015:Jan.)
• Page Start: 82
• Page End: 98
• Publication Date: 2014-05-06
• Subjects: Information science -- Periodicals; Information technology -- Periodicals; 020.5
• DOI: 10.1002/asi.23163
• Languages: English
• ISSNs: 2330-1635

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Automatic image annotation plays a critical role in modern keyword‐based image retrieval systems. For this task, the nearest‐neighbor–based scheme works in two phases: first, it finds the most similar neighbors of a new image from the set of labeled images; then, it propagates the keywords associated with the neighbors to the new image. In this article, we propose a novel approach for image annotation, which simultaneously improves both phases of the nearest‐neighbor–based scheme. In the phase of neighbor search, different from existing work discovering the nearest neighbors with the predicted distance, we introduce a ranking‐oriented neighbor search mechanism (RNSM), where the ordering of labeled images is optimized directly without going through the intermediate step of distance prediction. In the phase of keyword propagation, different from existing work using simple heuristic rules to select the propagated keywords, we present a learning‐based keyword propagation strategy (LKPS), where a scoring function is learned to evaluate the relevance of keywords based on their multiple relations with the nearest neighbors. Extensive experiments on the Corel 5K data set and the MIR Flickr data set demonstrate the effectiveness of our approach.</p> </abstract>