Image content classification using a block Kolmogorov Complexity measure

Zheru Chi, Jun Kong

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)


Image content classification is a very important step in document image analysis and understanding, and page-segmentation-based document image compression. In this paper, we present an new approach to classify image content using block Kolmogorov Complexity (KC) measures. A binarized two-dimensional image is first partitioned into blocks and each block image is converted into a one-dimensional binary sequence using either horizontal or vertical scanning. The block complexities are then computed over the obtained binary sequences. An image is classified into one of two categories, textual or pictorial images, using two fuzzy rules with the mean value and the standard deviation of block complexities. Experimental results on eight Chinese/English textual images of different fonts and eight different pictorial images show that our approach is reliable in discriminating these two types of images. Moreover, the performance of our method, where an training process is not required, is comparable to that of a neural network technique.
Original languageEnglish
Title of host publicationInternational Conference on Signal Processing Proceedings, ICSP
Number of pages4
Publication statusPublished - 1 Dec 1998
EventProceedings of the 1998 4th International Conference on Signal Processing Proceedings, ICSP '98 - Beijing, China
Duration: 12 Oct 199816 Oct 1998


ConferenceProceedings of the 1998 4th International Conference on Signal Processing Proceedings, ICSP '98

ASJC Scopus subject areas

  • Signal Processing

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