Abstract
Technological advances in digital imaging, broadband networking, and data storage have motivated people to communicate and express by sharing images, video, and other forms of media online [1]. Although the problems of acquiring, storing and transmitting the images are well addressed, capabilities to manipulate, index, sort, filter, summarize, or search through image database lack maturity. Modern image search engines [1–3, 30] retrieve the images based on their visual contents, commonly referred to as Content Based Image Retrieval (CBIR) systems. CBIR systems have found applications in various fields like fabric and fashion design, interior design as panoramic views [5, 20, 38–41], art galleries [22], museums, architecture/ engineering design [22], weather forecast, geographical information systems, remote sensing and management of earth resources [40, 41], scientific database management, medical imaging, trademark and copyright database management, the military, law enforcement and criminal investigations [41], intellectual property, picture archiving and communication systems, retailing and image search on the Internet (very general image content).
Keywords
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Kekre, H.B., Sarode, T.K., Thepade, S.D., Vaishali, V. (2011). Improved texture feature based image retrieval using Kekre’s fast codebook generation algorithm. In: Pise, S.J. (eds) Thinkquest~2010. Springer, New Delhi. https://doi.org/10.1007/978-81-8489-989-4_26
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DOI: https://doi.org/10.1007/978-81-8489-989-4_26
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