NDLI: Content-based Image Retrieval for Image Indexing

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Content-based Image Retrieval for Image Indexing

Content Provider	Semantic Scholar
Author	Bhuiyan, Al-Amin
Copyright Year	2015
Abstract	Content-based image retrieval has attained a position of overwhelming dominance in computer vision with the advent of digital cameras and explosion of images in the Internet and Clouds. Finding the most relevant images in a short time is a challenging job with many big cloud sites competing in image search in terms of accuracy and recall. This paper addresses an image retrieval system employing color information indexing. The system is organized with the hue components of the HSV color model. To assess the precision of the image retrieval system, experiments have been carried out on a database consisting of 450 images drawn by Japanese traditional painters, namely Sharaku, Hokusai, Hiroshige, and the images obtained from the World Wide Web (WWW) multicolor natural scenes. In order to query the database, the user specifies an object on which the same color attributes are evaluated and all similar looking images are exposed as the outcomes of the query. Keywords—color indexing; HSV color model; color histogram; Minkowski distance metric; fuzzy clustering; Color Quantization
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://thesai.org/Downloads/Volume6No6/Paper_11-Content_based_Image_Retrieval_for_Image_Indexing.pdf
Alternate Webpage(s)	http://www.thesai.org/Downloads/Volume6No6/Paper_11-Content_based_Image_Retrieval_for_Image_Indexing.pdf
Language	English
Access Restriction	Open
Subject Keyword	Addresses (publication format) Cluster analysis Color histogram Color quantization Computer vision Content-based image retrieval Digital camera Experiment Fuzzy clustering Gene Distance Metric Indexes Internet Question (inquiry) WWW World Wide Web statistical cluster
Content Type	Text
Resource Type	Article

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