RSOM tree and class specific hyper graph based distributed image retrieval

Rui Song; Shengping Xia; Jianjun Liu

doi:10.1117/12.832355

30 October 2009 RSOM tree and class specific hyper graph based distributed image retrieval

Rui Song, Shengping Xia, Jianjun Liu

Author Affiliations +

Proceedings Volume 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques; 749704 (2009) https://doi.org/10.1117/12.832355
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

Retrieval of images containing a given object from a large image database has attracted much attention from various researchers. These images may have been taken from different viewpoints under various imaging parameters with different backgrounds, and the target objects might be occluded. In order to solve the problem, a new method has been proposed in this paper. Firstly, some preliminary works of our method have been introduced. After features extracted from images, a recursive self-organizing mapping tree can be used to save the similarities between them. And the classspecific hyper-graphs have been used to index images belonging to same objects. When retrieving images, the RSOM tree can be used to learn how many common features there are between the querying image and the images in the database, and the response images could be sorted by these similarities. Using the response images from earlier queries as the new querying inputs, all responses can be retrieved with the generic query expansion strategy recursively. Subsequent re-queries can be simplified with all images indexed by CSHGs. There is innate concurrency in the method proposed, which makes it suitable for large scale applications such as Internet services.

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Rui Song, Shengping Xia, and Jianjun Liu "RSOM tree and class specific hyper graph based distributed image retrieval", Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 749704 (30 October 2009); https://doi.org/10.1117/12.832355

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KEYWORDS

Image retrieval

Feature extraction

Databases

Image processing

Internet

Medical imaging

Visualization

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