Search results for key=MSM1999c : 1 match found.

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  • @inproceedings{MSM1999c,
    	vgclass =	{fullconf},
    	vgproject =	{viper,cbir},
    	author =	{Henning M\"{u}ller and David McG. Squire and Wolfgang
    	M\"{u}ller and Thierry Pun},
    	title =	{Efficient access methods for content-based image retrieval
    	with inverted files},
    	editor =	{Sethuraman Panchanathan and Shih-Fu Chang and C.-C. Jay
    	booktitle =	{Multimedia Storage and Archiving Systems IV (VV02)},
    	address =	{Boston, Massachusetts, USA},
    	volume =	{3846},
    	series =	{SPIE Proceedings},
    	month =	{September~20--22},
    	year =	{1999},
    	note =	{(SPIE Symposium on Voice, Video and Data Communications)},
    	url =	{/publications/postscript/1999/MuellerHSquireMuellerWPun_msasIV.pdf},
    	url1 =	{/publications/postscript/1999/},
    	abstract =	{As human factor studies over the last thirty years have
    	shown, response time is a very important factor for the usability of an
    	interactive system, especially on the world wide web. In particular,
    	response times of under one second are often specified as a usability
    	requirement \cite{Nie97}.  This paper compares several methods for
    	improving the evaluation time in a content-based image retrieval system
    	(CBIRS) which uses inverted file technology.  The use of the inverted
    	file technology facilitates search pruning in a variety of ways, as is
    	shown in this paper. For large databases ($> 2000$ images) and a high
    	number of possible features ($> 80000$), efficient and fast access is
    	necessary to allow interactive querying and browsing. Parallel access
    	to the inverted file can reduce the response time. This parallel access
    	is very easy to implement with little communication overhead, and thus
    	scales well. Other search pruning methods, similar to methods used in
    	information retrieval, can also reduce the response time significantly
    	without reducing the performance of the system.  The performance of the
    	system is evaluated using precision vs. recall graphs, which are an
    	established evaluation method in information retrieval. A  user survey
    	was carried out in order to obtain relevance judgments for the queries
    	reported in this work.},