Research On The Key Technologies Of Web Image Retrieval

Web image retrieval technology is a very important content of information retrieval, and it is also a research focus in the field of image processing and computer vision. The technology provide the relevant graphic and image retrieval services for users by analyzing visual features of the web image. Its main aim is to overcome the constraints of traditional keyword query, help users to search out the precise information in the vast reservoir of information as soon as possible,and achieves the function of ’what you see is what you want to search’. This technology has been widely used in web image search engine, trademark retrieval, digital museum, electronic commerce and so on. But, there are still many problems to be solved recently. The complexity and diversity of web image lead the feature extraction and image matching to some difficult condition, which decrease the retrieval precision; the real-time of web image retrieval decreased because of the database size of web image increased rapidly.This dissertation analyses the methods of image retrieval based on visual features, focus on the study of images’ local feature extraction algorithms and relevance feedback. Aiming at each keypoint of web image retrieval, it proposed the spatial division algorithms based on the stable interest points, image retrieval algorithms based on invariant features of salient region and the optimized feedback. Then, it put forward a new kind of web image intelligent retrieval system and introduce the proposed to it. The main work and contribution are summarized as follows.1. We propose an image retrieval algorithm based on stable interest points and their region distribution(SIPRD). Aiming at the problem of that the traditional interest points detection algorithm can produce the detection position error and false detection when it extracts the web image features, we analyse the advantages and disadvantages of the performance of some gray-based interest points detection algorithms, introduce an interest point detector with optimized gradient filter(ODF) to extract the stable interest points of normalized images, and reduce the interference of unstable interest points; using the fearture of stable interet points, which has a high information content, and according to the spatial distribution of them, we devide the image into annular or convex hull, meanwhile, descript the web images’ feartures with the weighted feature vector, such as color histogram of convex hull and the annular color histogram. The experimental results show that our algorithm can effectively decrease theimpact of the unstable points of interest, increase the accuracy of region division, and promote the performance of retrieval.2. We proposed an interest point detection algorithm based on scale invariant features(IPDSH). Aiming at the problem of that the web image with different resolution can lose the interest point information or produce the pseudo interest when it is attacked by scale or affine transformations, which will generally result in inconsistency problem of interest points detection between images with the same content, we use the Gauss kernel with different scale factor to convolute the image, so as to obtain the multi-scale spaces. We compare the pixels in multi-scale spaces with the pixels in its adjacent scale spaces to get the extreme points, calculate the gradient change values in the neighborhood of extreme points of each scale spaces and sort by size, retain the extreme points with larger gradient change values in the neighborhood of each scale spaces according to the set threshold, and regard the extreme point as the stable interest points in scale space. Experimental results show that the proposed method is of higher speed of retrieval and robustness to rotation. It can reduce the impact of unstable interest points and improve the precision of retrieval.3. We proposed a new retrieval method based on salient region and invariant features(SRIF). Aiming at the problem of that the algorithms based on interest points’ distribution are susceptible to the free interest points in images’ background, which leads to the decrease of accuracy when it need to extract the web images’ features, we detect the stable interest points by using the IPDSH algorithm. According to the distribution of the stable interest points, we detect the salient region with the traversal method, and confirm the salient interest points in scale spaces. Finally, we calculate the pseudo-Zernike moments of the salient interest points’ neighborhood as the feature vectors. Experimental results show that the proposed method is more in line with the principle of human vision, can effectively avoid wrongly picking the object of the image by using raw image segmentation algorithms, increase the reliability of salient region and the salient points, and improve the retrieval accuracy. Besides, the features extracted by the algorithm is scale invariant, less susceptible to noise interference and strong robustness.4. We proposed an optimal feedback algorithm based on ecological selection particle swarm(r/KPSO-RF). In traditional relevance feedback algorithms(RF), the scale-parameters adjustment depends on predetermined criteria and there is no flexible adaptive adjustment space, which make the algorithms efficiency degradation. Aiming at this problem, we regarded the images as particles and initialized them. Then, the swarm intelligence optimization algorithm(r/KPSO) is introduced into the feedback process. Using the characteristics of global optimization and fast convergence, the algorithm guide the motion direction of particles to close to the optimal solution set rapidly with the ideal supervision. Finally, according to the optimal results, the algorithm can adaptively adjust the parameters’ weight, which can help in improving the performance of feedback. The experimental results show that the algorithm can accurately understand the users’ real intention from their feedback and effectively solve the randomicity of optimal targets’ detail.5. We proposed a new web intelligent image retrieval system(WIIRS). In-depth research and analysis is conducted on the framework of the image retrieval system based on the web. WIIRS is consisting of three parts: capture module, management module and detection module. It collect and identifier the web images by using the faculty of capture module, and besides build the image database; it descript the image features by using the faculty of detection module, and besides build image features database; it maintain the stability of the system and realize the faculty of human-computer interaction through the management module. Finally, based on the WIIRS, a pornographic image retrieve and filtering system based on Internet(PIRF) was designed to improving the detecting precision of pornographic images’ filtering. Experimental results show that WIIRS can effectively realize the faculty of web image retrieval and PIRF can effectively detect and filter the pornographic images. All of them have high practical values.