Using a region and visual word approach towards semantic image retrieval

Kalantidis, Y; Spyrou, E; Mylonas, P; Kollias, S

dc.contributor.author	Kalantidis, Y	en
dc.contributor.author	Spyrou, E	en
dc.contributor.author	Mylonas, P	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T02:47:08Z
dc.date.available	2014-03-01T02:47:08Z
dc.date.issued	2010	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/33013
dc.subject	Image Retrieval	en
dc.subject	k-means clustering	en
dc.subject	Semantic Similarity	en
dc.subject	Texture Features	en
dc.subject	Bag of Words	en
dc.subject.other	Bag of words	en
dc.subject.other	Color and texture features	en
dc.subject.other	Database images	en
dc.subject.other	Image regions	en
dc.subject.other	K-means clustering	en
dc.subject.other	Model vectors	en
dc.subject.other	Model-based	en
dc.subject.other	Region types	en
dc.subject.other	Region-based	en
dc.subject.other	Semantic image retrieval	en
dc.subject.other	SIFT descriptors	en
dc.subject.other	Similar image	en
dc.subject.other	Visual vocabularies	en
dc.subject.other	Visual word	en
dc.subject.other	Clustering algorithms	en
dc.subject.other	Image retrieval	en
dc.subject.other	Semantics	en
dc.subject.other	Image segmentation	en
dc.title	Using a region and visual word approach towards semantic image retrieval	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/SMAP.2010.5706869	en
heal.identifier.secondary	http://dx.doi.org/10.1109/SMAP.2010.5706869	en
heal.identifier.secondary	5706869	en
heal.publicationDate	2010	en
heal.abstract	This paper presents a region-based approach towards semantic image retrieval. Combining segmentation and the popular Bag-of-Words model, a visual vocabulary of the most common ""region types"" is first constructed using the database images. The visual words are consistent image regions, extracted through a k-means clustering process. The regions are described with color and texture features, and a ""model vector"" is then formed to capture the association of a given image to the visual words. Opposite to other methods, we do not form the model vector based on all region types, but rather to a smaller subset. We show that the presented approach can be efficiently applied to image retrieval when the goal is to retrieve semantically similar rather than visually similar images. We show that our method outperforms the commonly used Bag-of-Words model based on local SIFT descriptors. © 2010 IEEE.	en
heal.journalName	Proceedings - 2010 5th International Workshop on Semantic Media Adaptation and Personalization, SMAP 2010	en
dc.identifier.doi	10.1109/SMAP.2010.5706869	en
dc.identifier.spage	85	en
dc.identifier.epage	89	en