To solve the "semantic gap" problem, image semantic understanding is the key technique. In this paper, firstly
analyzes the current situation of image semantic understanding research, which includes some methods of image
semantic representation and image semantic extraction, and then it discusses the applications of image semantic
understanding, and finally it presents the trend about image semantic understanding.
Image semantic understanding is one of the most important techniques for solving the problem of semantic gap. By
introducing generalized computing into image semantic understanding, this paper presents a kind of third class image
description model. Then, under the guidance of the model, the approaches of image semantic information extraction is
proposed based on generalized set and generalized transformation. Finally, a kind of image semantic understanding
system based on generalized is sketched out.
Image segmentation is one of the most attractive problems in image processing. In image segmentation how to extract useful features from image has become crucial. However, color feature or texture feature, which are both wildly used features, could not process segmentation problem alone very well, especially when images are complex. We adopt a rough-fuzzy set approach, which can properly process high dimensionality, for image segmentation considering both color and texture features. This approach firstly constructs a structure named fuzzy data cube, whose attributes are composed of the fuzzy sets associated with image features. The fuzzy data cube, which can be two-dimension or high-dimension, is as the basic data structure in this method. A definition of the membership function of similarity relation based rough-fuzzy set is introduced as well as the definition of dependency function to evaluate the importance of an attribute for image segmentation. Then we used the rough-fuzzy set to discover the similarity set in fuzzy data cube to obtain the segmentation result. Experiments on mosaic and natural images are presented to demonstrate the effectiveness of the proposed method.
Data compression is one of key techniques in image processing. To begin with, this paper discusses the actuality of image compression coding, and then introduces generalized computing concept. Finally, by introducing knowledge to date compression, this paper presents a kind of intelligent image compression model (IICM) based on generalized computing.
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