Abstract
Content-Based Image Retrieval (CBIR) is also known as Query By Image Content (QBIC) is the application of computer vision techniques and it gives solution to the image retrieval problem such as searching digital images in large databases. The need to have a versatile and general purpose Content Based Image Retrieval (CBIR) system for a very large image database has attracted focus of many researchers of information-technology-giants and leading academic institutions for development of CBIR techniques. Due to the development of network and multimedia technologies, users are not fulfilled by the traditional information retrieval techniques. So nowadays the Content Based Image Retrieval (CBIR) are becoming a source of exact and fast retrieval. Texture and color are the important features of Content Based Image Retrieval Systems. In the proposed method, images can be retrieved using color-based, texture-based and color and texture-based. Algorithms such as auto color correlogram and correlation for extracting color based images, Gaussian mixture models for extracting texture based images. In this study, Query point movement is used as a relevance feedback technique for Content Based Image Retrieval systems. Thus the proposed method achieves better performance and accuracy in retrieving images.
Highlights
Requirement is to have a technique that can search and retrieve images in a manner that is both time efficient
Query point movement is used as a relevance feedback technique for Content Based Image Retrieval systems
A global segmentation, image feature extraction, representation, descriptor uses the visual features of the whole image, mapping of features to semantics, storage and indexing, whereas a local descriptor uses the visual features of image similarity-distance measurement and retrieval which makes Content-Based Image Retrieval (CBIR) system development as a challenging regions or objects to describe the image content
Summary
Requirement is to have a technique that can search and retrieve images in a manner that is both time efficient. Visual content can be very general or domain databases based on users’ interests. These techniques includes several areas such as image content descriptor can be either global or local. A global segmentation, image feature extraction, representation, descriptor uses the visual features of the whole image, mapping of features to semantics, storage and indexing, whereas a local descriptor uses the visual features of image similarity-distance measurement and retrieval which makes CBIR system development as a challenging regions or objects to describe the image content. A more complex way of dividing an image, is to undertake a complete object segmentation to obtain semantically meaningful objects (like ball, car, horse). Automatic object segmentation for broad domains of general images is unlikely to succeed
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