AN ADAPTIVE CLASSIFICATION ALGORITHM OF PARETO METHOD AND TOPOLOGY PRESERVE HASHING FOR QUERY-BY- MULTIPLE IMAGE RETRIEVAL SYSTEM

Image Retrieval is a technique of searching, browsing, and retrieving the images from an image database. There are two types of dif erent image retrieval techniques namely text based image retrieval and content based image retrieval techniques. Text- Based image retrieval uses traditional database techniques to manage images. Content-based image retrieval (CBIR) uses the visual features of an image such as color, shape, texture, and spatial layout to represent and index the image. This combination of features provides a robust feature set for image retrieval. Evaluate the performance of proposed methods at dif erent precision value of the image retrieval on each category of image database. Recent research indicates that since these features are not exactly associated with image semantic meaning, query-by-one (QBO), which means to query with only one image, usually is insuf icient to achieve good performance. Thus, query-by-multiple (QBM) methods are introduced and applied in many content-based image retrieval systems. However, how to maximize major features and minimize minor ones of these inputs while matching could influence retrieval results significantly. This technique is proposed novel multiple- query information retrieval algorithm that combines the Pareto front method with efficient Manifold ranking. Uniquely, to decrease the complexity of user input and reduce user-computer interaction, a topology preserve hashing algorithm will be introduced