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Parameter estimation of the extended generalized gaussian family distributions using maximum likelihood scheme.

P Mercy Nesa Rani, A Saravanan, Y Chen,JZ Wang, R Krovetz, Y Chen, JZ Wang,C Faloutsos,R Barber,M Flickner,J Hafner,W Niblack,D Petkovic,W Equitz, MAT Figueiredo,A Vailaya, AK Jain,HJ Zhang, M Flickner,H Sawhney, W Niblack,J Ashley, QH Dom,Y Gdalyahu,D Weinshall,M Werman,C Carson,S Belongie,H Greenspan,J Malik,A Mueen, MS Baba,R Zainuddin, JA Hartigan, MA Wong,R Jain,SNJ Murthy, PLJ Chen,S Chatterjee,J Li,JZ Wang, G Wiederhold,WY Ma,BS Manjunath,A Natsev, R Rastogi, K Shim, TJ Jose, P Mythili, CC Yang, J Shi, J Malik,AWM Smeulders,M Worring, S Santini, A Gupta, R Jain,JR Smith, SF Chang, A Vailaya, A Jain, HJ Zhang, G Wiederhold, J Li,JZ Wang,J Li, G Wiederhold,E Nasibov,S Peker, K Premalatha, AM Natarajan, JY Lee

Research Journal of Information Technology(2003)

引用 14|浏览17
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摘要
This study attempts to provide fast indexing technique which will help to retrieve images from the database quickly and focuses on how to retrieve most relevant images from the database. The need for efficient Content-based Image Retrieval (CBIR) has increased tremendously in many application areas such as biomedicine, military, commerce, education and web image classification and searching. The semantic gap is the greatest challenge in the CBIR. The semantic gap is the lack of coincidence between the information that one can extract from the visual data and the interpretation that the same data have for a user in a given situation. The CBIR uses the visual contents of an image such as color, shape, texture and spatial layout to represent and index the image. In typical content-based image retrieval systems, the visual contents of the images in the database are extracted and described by multi-dimensional …
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