Study On Theory And Approach Of Image Feature Extraction And Content-based Image Database Retrieval

Image database retrieval is a hot topic and has attracted increased attention from researchers. It includes several contents such as describing the image visual content (extracting features), image database management, image matching and so on. This dissertation deals with the content-based image retrieval (CBIR) theory and technique; some new features and tools for more concisely and discriminatingly charactering the content of an image are proposed, such as region-based color histogram, grey-primitive co-occurrence matrix, ratio of centripetal moment, ratio of eccentric moment and ratio of inertial moment. A new modified genetic algorithm is also described in this dissertation, which can upgrade the performance of standard genetic algorithm (SGA) while used in image segmentation.Color feature is often used to describe image content. Color histogram built from cumulative distributions of content colors is a main color feature using in image retrieval. However, the color histogram does not contain the information of spatial distribution of colors across an image. So there might be different images which possess different contents but share with same color histogram. To deal with this problem effectively, a region-based color histogram (RBCH) approach is proposed in this dissertation. In RBCH, an image is first decomposed into several subregions(blocks) with pyramid data structure, and the subregion color histograms are built correspondingly. After that, an integrated color histogram is built based on the subregion color histograms. This integrated color histogram overcomes the disadvantage of simple global color histogram. To digitally represent a color histogram an inertial moment ratio calculated from a color histogram graph is proposed as a new digital feature of the color histogram.It is more efficient to use texture feature to describe an image with complicated textural contents. Grey co-occurrence matrix, which is a 2D color statistical feature, is often used to analyze such images. For the vivid textural structure images it had better use the structural approach to characterize image content. In this dissertation a new statistical tool called as grey-primitive co-occurrence matrix is proposed, which is based on predefined specific primitive texture structures. The features extracts from a set of this type matrix can be formed as a primitive texture feature vector and used in retrieving images from image database. The grey-primitive co-occurrence matrix based approach provides better performance than conventional grey co-occurrence matrix based approach.Image segmentation is very important in object detection, feature extraction and object recognition processing. Object outline detection must be done before shape features are extracted. Recently single-threshold or multi-threshold is often used to segment image and detect object contour on an image by means of genetic algorithm. A modified genetic algorithm is proposed. In the dissertation a different fitness function isconstructed and similarity is introduced, which can increase variety of population md avoid prematurity.Shape feature is the most suitable tool in characterizing image content object which has clear object outline. Even an object contour has been extracted, it is still difficult in constructing suitable shape features to describe the shape discriminatingly and efficiently. Some new features such as ratio of centripetal moment, ratio of eccentric moment and ratio of inertial moment are introduced in this dissertation. These features have the properties of scale-invariance-, rotation-invariance and translation-invariance, and can be used well in depicting the linearized outline object.Support Vector Machine (SVM) is a new classification method. SVM and its implementation technique SMO can be used for image retrieval; however, they were originally developed for dealing with two-classification problem, so a binary tree and a matrix for classification are constructed for solving multi-classification problem. Feature vectors...