Object-Based Classification of Mixed-Mode Images

Authors:
Hua Cai;Bing Zeng
Affiliations:
-;-
Venue:
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Year:
2001

Citing 2
Cited 0

Text segmentation in mixed-mode images using classification trees and transform tree-structured vector quantization

ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 04
Context-based multiscale classification of document images using wavelet coefficient distributions

IEEE Transactions on Image Processing

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Abstract

This paper presents an efficient algorithm of classifying mixed-mode images into "objects" of rectangular shape and with parent-children relationship. We consider four different classes of "objects": background, text, graph, and photograph. The classification algorithm has the hierarchical nature, i.e., it tries to (1) classify background regions and non-background regions, (2) classify bi-level objects and multi-level objects (in non-background regions), and (3) classify graph and photograph (in multi-level objects). During the classification, a merging-and-splitting refinement is used for boundaries and small fragments. Excellent classification results have been observed in all experimental tests.