NDLI: High-Level Concept Detection Based on Mid-Level Semantic Information and Contextual Adaptation

Content Provider	IEEE Xplore Digital Library
Author	Mylonas, P. Spyrou, E. Avrithis, Y.
Copyright Year	2007
Description	Author affiliation: Nat. Tech. Univ. of Athens Image, Athens (Mylonas, P.; Spyrou, E.; Avrithis, Y.)
Abstract	In this paper we propose the use of enhanced mid-level information, such as information obtained from the application of supervised or unsupervised learning methodologies on low-level characteristics, in order to improve semantic multimedia analysis. High-level, a priori contextual knowledge about the semantic meaning of objects and their low-level visual descriptions are combined in an integrated approach that handles in a uniform way the gap between semantics and low-level features. Prior work on low-level feature extraction is extended and a region thesaurus containing all mid-level features is constructed using a hierarchical clustering method. A model vector that contains the distances from each mid-level element is formed and a neural network-based detector is trained for each semantic concept. Contextual adaptation improves the quality of the produced results, by utilizing fuzzy algebra, fuzzy sets and relations. The novelty of the presented work is the context- driven mid-level manipulation of region types, utilizing a domain-independent ontology infrastructure to handle the knowledge. Early experimental results are presented using data derived from the beach domain.
Starting Page	193
Ending Page	198
File Size	474735
Page Count	6
File Format	PDF
ISBN	0769530400
DOI	10.1109/SMAP.2007.38
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-17
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy sets Algebra Clustering methods Neural networks Detectors Ontologies Feature extraction Thesauri Unsupervised learning Information analysis
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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