NDLI: Shape annotation for intelligent image retrieval

Content Provider	Springer Nature Link
Author	Castella, Giovanna Fanelli, Anna M. Sforza, Gianluca Torsello, M. Alessandra
Copyright Year	2015
Abstract	Annotation of shapes is an important process for semantic image retrieval. In this paper, we present a shape annotation framework that enables intelligent image retrieval by exploiting in a unified manner domain knowledge and perceptual description of shapes. A semi-supervised fuzzy clustering process is used to derive domain knowledge in terms of linguistic concepts referring to the semantic categories of shapes. For each category we derive a prototype that is a visual template for the category. A novel visual ontology is proposed to provide a description of prototypes and their salient parts. To describe parts of prototypes the visual ontology includes perceptual attributes that are defined by mimicking the analogy mechanism adopted by humans to describe the appearance of objects. The effectiveness of the developed framework as a facility for intelligent image retrieval is shown through results on a case study in the domain of fish shapes.
Starting Page	179
Ending Page	195
Page Count	17
File Format	PDF
ISSN	0924669X
Journal	Applied Intelligence
Volume Number	44
Issue Number	1
e-ISSN	15737497
Language	English
Publisher	Springer US
Publisher Date	2015-08-05
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Shape annotation Fuzzy shape clustering Image annotation Image retrieval Semi-supervised clustering Visual ontology Artificial Intelligence (incl. Robotics) Mechanical Engineering Manufacturing, Machines, Tools
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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