Research On The Key Technologies Of Content-Based Image Retrieval

Visional feature extraction, high dimensional indexing mechanism and relevance feedback are three important issues in content-based image retrieval. Low-level features which reflect the image content is the necessity for improvement in the image retrieval performance, the Relevance feedback techniques are important approaches closing up the semantic gap between high-level concepts and low-level features in image retrieval effectively, and efficient indexing schemes for high-dimensional data are required for real-time retrieval in large-scale image database.Research on the feature extraction in color and shape has been done. The color histogram does not contain the information of spatial distribution of colors across an image. To deal with this problem effectively, a color feature extraction approach based on domain color partition is proposed in the dissertation. Color histogram is used to select the domain colors of the image and the maximum entropy algorithm is applied to extract the region information of selected colors. At last, similarity measurement is defined. Image segmentation is very important in object detection, feature extraction processing. Object outline detection must be done before shape features are extracted. Based on texture element and based self-adaptive network model, two methods are proposed to segment images and detect objects contour. Then Canny contour depiction is introduced to describe the shape.Accurate estimate of data distribution and efficient partition of data space are key problems in high-dimensional indexing schemes. In this dissertation, a hierarchical indexing scheme based on the lattice vector quantization (LVQ) indexing method is proposed for higher performance. Gaussian mixture distribution is used to model the feature space and lattice vector quantizers are trained to partition simplified Gaussian clustering. On the foundation of feature space partition and Gaussian clustering lattice vector quantization, hierarchical structure is used to organize the data. At last, maintenance of the indexing is introduced. Experiments on a large real-world dataset demonstrate a remarkable reduction of the amount of accessed vectors in k - NNsearches and a better performance is achieved compared with existing indexing schemes.Extracting multi-feature suitable to representing query concepts of users from feedback samples and adjusting of vector element's weights by combining the negative samples and positive samples are two key problems for relevance feedback techniques. In the dissertation, a relevance feedback method based on multi-feature extraction (BMFE) is proposed for content-based image retrieval. By analyzing the relevance of the negative example and positive, the global dispersion formulation is designed. Lagrange multipliers are used to resolve the dispersion matrix. The BMEF approach not only dynamically adjusts the vector element's weight, but also reflects the concepts of the users.At last, the prototype content-based image retrieval system programmed with Java is introduced in this dissertation. The system is possessed with feature extraction, high-dimension indexing schemes and relevance feedback using the skills proposed in the forecited sectors.