28 January 2008 Exploring the relationships of regions for visual content understanding
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An image can be considered as a collection of small regions. Most researches of image understanding extract features of these regions, and investigate relationships between these regions and keywords of images that are annotated manually. There are also some researches that explore the ontology of words. However, little attention has been paid to the relationships among regions in an image. In this paper, we make a close study of this type of relationships without the assumption that they are independent for visual content understanding. We first analyze the co-occurrence of regions using a statistical relevance probability model (SRP). Since human attention in the perception process of an image first focuses in one region and then moves on to other relevant regions, we propose a novel model called region sequence prediction model (RSP) to describe it. In RSP, annotation keywords for region sequences of the image and their probabilities are generated by a hidden Markov model. Experimental results of both image annotation and retrieval on the Corel dataset (an open image dataset) show that mining the relationships of image regions will achieve comparative or better performance in visual content understanding.
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Ting Liu, Ting Liu, Weiqiang Wang, Weiqiang Wang, Yonghong Tian, Yonghong Tian, Tiejun Huang, Tiejun Huang, } "Exploring the relationships of regions for visual content understanding", Proc. SPIE 6822, Visual Communications and Image Processing 2008, 68220G (28 January 2008); doi: 10.1117/12.766068; https://doi.org/10.1117/12.766068


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