Object-Based Classification of Mixed-Mode Images
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Locating Text in Images using Matched Wavelets
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Automatic categorization of figures in scientific documents
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Image coding based on multiband wavelet and adaptive quad-tree partition
Journal of Computational and Applied Mathematics - Special issue: The international symposium on computing and information (ISCI2004)
Visual similarity based document layout analysis
Journal of Computer Science and Technology - Special section on China AVS standard
Deriving knowledge from figures for digital libraries
Proceedings of the 16th international conference on World Wide Web
Segregating and extracting overlapping data points in two-dimensional plots
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
Document retrieval using image features
Proceedings of the 2010 ACM Symposium on Applied Computing
Applying preattentive visual guidance in document image analysis
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
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In this paper, an algorithm is developed for segmenting document images into four classes: background, photograph, text, and graph. Features used for classification are based on the distribution patterns of wavelet coefficients in high frequency bands. Two important attributes of the algorithm are its multiscale nature-it classifies an image at different resolutions adaptively, enabling accurate classification at class boundaries as well as fast classification overall-and its use of accumulated context information for improving classification accuracy