The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Content Based Image Retrieval Using Active-Nets
|
|
Author(s): David García Pérez (University of Santiago de Compostela, Spain), Antonio Mosquera (University of Santiago de Compostela, Spain), Stefano Berretti (University of Firenze, Italy)and Alberto Del Bimbo (University of Firenze, Italy)
Copyright: 2009
Pages: 30
Source title:
Artificial Intelligence for Maximizing Content Based Image Retrieval
Source Author(s)/Editor(s): Zongmin Ma (Northeastern University, China)
DOI: 10.4018/978-1-60566-174-2.ch005
Purchase
|
Abstract
Content-based image retrieval has been an active research area in past years. Many different solutions have been proposed to improve performance of retrieval, but the large part of these works have focused on sub-parts of the retrieval problem, providing targeted solutions only for individual aspects (i.e., feature extraction, similarity measures, indexing, etc). In this chapter, we first shortly review some of the main practiced solutions for content-based image retrieval evidencing some of the main issues. Then, we propose an original approach for the extraction of relevant image objects and their matching for retrieval applications, and present a complete image retrieval system which uses this approach (including similarity measures and image indexing). In particular, image objects are represented by a two-dimensional deformable structure, referred to as “active net.” Active net is capable of adapting to relevant image regions according to chromatic and edge information. Extension of the active nets has been defined, which permits the nets to break themselves, thus increasing their capability to adapt to objects with complex topological structure. The resulting representation allows a joint description of color, shape, and structural information of extracted objects. A similarity measure between active nets has also been defined and used to combine the retrieval with an efficient indexing structure. The proposed system has been experimented on two large and publicly available objects databases, namely, the ETH-80 and the ALOI.
Related Content
|
Aswathy Ravikumar, Harini Sriraman.
© 2023.
18 pages.
|
|
Ezhilarasie R., Aishwarya N., Subramani V., Umamakeswari A..
© 2023.
10 pages.
|
|
Sangeetha J..
© 2023.
13 pages.
|
|
Manivannan Doraipandian, Sriram J., Yathishan D., Palanivel S..
© 2023.
14 pages.
|
|
T. Kavitha, Malini S., Senbagavalli G..
© 2023.
36 pages.
|
|
Uma K. V., Aakash V., Deisy C..
© 2023.
23 pages.
|
|
Alageswaran Ramaiah, Arun K. S., Yathishan D., Sriram J., Palanivel S..
© 2023.
17 pages.
|
|
|