nana crosby - Academia.edu (original) (raw)
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Papers by nana crosby
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2010
Graph data mining is an active research area. Graphs are general modeling tools to organize infor... more Graph data mining is an active research area. Graphs are general modeling tools to organize information from heterogenous sources and have been applied in many scientific, engineering, and business fields. With the fast accumulation of graph data, building highly accurate predictive models for graph data emerges as a new challenge that has not been fully explored in the data mining community.
ABSTRACT After image retrieval based on wide baseline match, verification is required since there... more ABSTRACT After image retrieval based on wide baseline match, verification is required since there are still many false match pairs. The popular verification method, geometry, is limited for example in a weak match situation where the number of match pairs is less than enough. In order to improve retrieval results in such cases, this paper puts forward color pattern diffusion verification. Firstly, HSI Color moments in the original region where two corresponding SIFT feature points are detected are extracted from both the query image and the database image and then matched with each other. Secondly, color moments in both diffusion regions are extracted and matched as well. Finally, if the matching similarity of above color moments keeps invariant with color pattern diffusion in image space, the two interest points is taken as a real corresponding pair. Experiments demonstrated this approach works better with near real-time performance in many practical situations, especially in the weak match situation context.
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2010
Graph data mining is an active research area. Graphs are general modeling tools to organize infor... more Graph data mining is an active research area. Graphs are general modeling tools to organize information from heterogenous sources and have been applied in many scientific, engineering, and business fields. With the fast accumulation of graph data, building highly accurate predictive models for graph data emerges as a new challenge that has not been fully explored in the data mining community.
ABSTRACT After image retrieval based on wide baseline match, verification is required since there... more ABSTRACT After image retrieval based on wide baseline match, verification is required since there are still many false match pairs. The popular verification method, geometry, is limited for example in a weak match situation where the number of match pairs is less than enough. In order to improve retrieval results in such cases, this paper puts forward color pattern diffusion verification. Firstly, HSI Color moments in the original region where two corresponding SIFT feature points are detected are extracted from both the query image and the database image and then matched with each other. Secondly, color moments in both diffusion regions are extracted and matched as well. Finally, if the matching similarity of above color moments keeps invariant with color pattern diffusion in image space, the two interest points is taken as a real corresponding pair. Experiments demonstrated this approach works better with near real-time performance in many practical situations, especially in the weak match situation context.