Generating graphs from key points for near-duplicate document image matching
Li Liu
University of Shanghai for Science and Technology, China
: J Comput Eng Inf Technol
Abstract
We propose a novel near-duplicate document image matching approach. Some key points are first detected from the image using the difference-of-Gaussian function. We then present a clustering method, based on which the key points are clustered into several groups. The number of clusters is determined automatically according to the distributions of the key points. Afterwards, a graph is generated whose nodes correspond to the obtained clusters and the edges describe the relationships between two clusters. Consequently, the problem of image matching is transformed to graph matching. To compute the similarity between two graphs, we build their association graph and then find the maximum weight clique. A thorough evaluation of the performance of the proposed approach is conducted on two different datasets. Promising experimental results demonstrate the effectiveness and validity of this method.
Biography
Li Liu is a Lecturer at University of Shanghai for Science and Technology. She completed her PhD degree in Pattern Recognition and Intelligent System at East China Normal University, Shanghai, China, in 2015. She was a Visiting Doctoral Student at Centre for Pattern Recognition and Machine Intelligence (CENPARMI), Concordia University, Montreal, Quebec, Canada, from 2013 to 2014. Her research interests include Pattern Recognition, Machine Learning and Image Analysis.
Email: liuli_033@163.com
