Study On The Algorithms For Content-Based Image Retrieval

With the development of the multimedia network technology, the application of the image is extensive,the rapid increase of image application, Content-based Image Retrieval (CBIR) becomes most active one in multimediaretrieval field. In order to analysis the information included in an image, the CBIR system always analyses the color,texture, shape, and other low-layer image features, to establish retrieval vectors as retrieval index. In present time, the main CBIR method is similarity retrieval based on multi-dimension feature vector of image. Extracting features from image is the key issues in CBIR. In this paper, on the base of widely referring to the material about Content-Based Image Retrieval of home and abroad, the whole evolution of the theory on the Content-Based Image Retrieval technique and application study are reviewed at first, then the technology of Content-Based Image Retrieval and current situation are explained in an all-round way, and the key technology in the CBIR system is probed. In this dissertation I have further studied on the algorithm about the retrieval based on the low-layer image features of CBIR. Then I have present to use the associations of six pieces of features vector that are extract from the three image natural features which are color , texture and shape for form a new retrieval algorithm of using multiple features. The algorithm have been used on the discernment of adult image actually, and have certain discernment results. The color feature is the most simple and most convenient , effective feature of describing a image. It is the most extensive that use. Based on the human's psychology, the HSV color space is more ocular and easier to accept than the RGB color space. So in this paper the extracting of the color feature have dose in the HSV color space. While extracting,the image is transformed from the RGB color space to the HSV color space at first. At the same time, the vectors of s and v are mapping to the integer field for calculating amount that sparingly and raising the efficiency. Then the algorithm is calculating the Color Histogram based on the HSV color space and doing the non-equidistant quantization to reduce the dimension. Finally, the vector is the color feature vector which is a part of the multiple features vector. The texture feature is one of the important feature of the image, usually define a local property of the image, or to a kind of tolerance of the relation between pels in part area. Its essence is a space distribution law of grey level of neighborhood which portrays pels. The grey level co-occurrence matrix is a statistics measurement with two steps of the grey level of the image, and it is a means of the most frequently used to exacting texture features. There are 14 statistical values have been used to exacting texture features in the grey level co-occurrence matrix now. In this dissertation, I only use three of them to improve the calculate efficiency of the algorithm: contrast, energy and entropy. While exacting, the image needs to be transform from a color image to a grey image at first. The method of exacting texture features which based on the grey level co-occurrence matrix is based on the grey values of the image. In this dissertation the images which be used are all the color bitmapimages and it isn't have a grey value.So we needs to transform the image from the RGB color space to the YUV color space, and the matrix values of Y are the grey values. Then construct two directions co-occurrence matrixs which are θ=0o and θ=90o of the image, calculate the three values have been discussed above, finally the values are the texture feature vectors which are part of the multiple features vector. The shape feature is the most essential feature to portray objects, and it has to find some geometry invariants mainly for exacting shape features. The moment feature is one of the most important shape features of the image. It's construct on the statistic analysis of the distribution of grey values which inside a region. It's a description of the statistic average and the whole feature which can describe objects from the global point. Hu invariant moment is one of the most important shape invariant moments. In the algorithm, because of the shape feature is just the complementarity of the color feature and texture feature and to improve the calculate efficiency, it only used one step moment and two steps moment of the sevev shap invariant moment to be the shape feature vectors which are part of the multiple features vector. In this paper, the multiple features vector of the retrieval algorithm is a six dimensions vector. Every element of the vector has different magnitude from each other, so it must be a large deflection when measure the similar by the Euclidean distance. The deflection must be eliminate by the vector normalization method. When exacting is over, the algorithm uses the Gaussian normalization method to mormalizied the feature vector. Finally, it uses the Euclidean distance which is simplest and most frequently to use to be...