The process of limited-color image compression usually involves color quantization followed by palette re-indexing. Palette re-indexing could improve the compression of color-indexed images, but it is still complicated and consumes extra time. Making use of the topology-preserving property of self-organizing Kohonen feature map, we can generate a fairly good color index table to achieve both high image quality and high compression, without re-indexing. Promising experiment results will be presented.

News, magazines, Web pages, etc. in modern life always contain much text information. We propose a novel approach to detect text in images with very low false alarm rate. First of all, neural network color quantization is used to compact text color. Second, 3D histogram analysis chooses several color candidates, and then extracts each of these color candidates to obtain several bi-level images. Moreover, for each bi-level image, connectivity analysis and some morphological operators are fed to produce character candidates. Furthermore, we calculate some spatial features and relationships of each text candidate. Finally, we can localize text regions by authentication from LOG (Laplacian of Gaussian) edge detector. Meanwhile, in complex color images, multi-quantization layers can be integrated to reject non-text parts and reduce false alarm rate.