A stratification-based approach to accurate and fast image annotation

Authors:
Jianye Ye;Xiangdong Zhou;Jian Pei;Lian Chen;Liang Zhang
Affiliations:
Department of Computing and Information Technology, Fudan University, Shanghai, China;Department of Computing and Information Technology, Fudan University, Shanghai, China;School of Computing Science, Simon Fraser University, Canada;Department of Computing and Information Technology, Fudan University, Shanghai, China;Department of Computing and Information Technology, Fudan University, Shanghai, China
Venue:
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
Year:
2005

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Abstract

Image annotation is an important research problem in content-based image retrieval (CBIR) and computer vision with broad applications. A major challenge is the so-called “semantic gap” between the low-level visual features and the high-level semantic concepts. It is difficult to effectively annotate and extract semantic concepts from an image. In an image with multiple semantic concepts, different objects corresponding to different concepts may often appear in different parts of the image. If we can properly partition the image into regions, it is likely that the semantic concepts are better represented in the regions and thus the annotation of the image as a whole can be more accurate. Motivated by this observation, in this paper we develop a novel stratification-based approach to image annotation. First, an image is segmented into some likely meaningful regions. Each region is represented by a set of discretized visual features. A naïve Bayesian method is proposed to model the relationship between the discrete visual features and the semantic concepts. The topic-concept distribution and the significance of the regions in the image are also considered. An extensive experimental study using real data sets shows that our method significantly outperforms many traditional methods. It is comparable to the state-of-the-art Continuous-space Relevance Model in accuracy, but is much more efficient – it is over 200 times faster in our experiments.