This paper presents the design of an all-season image retrieval system. The system handles the images with and without distinct object(s) using different retrieval strategies. Firstly, based on the visual contrasts and spatial information of an image, a neural network is trained to pre-classify an image as distinct-object or no-distinct-object category by using the Back Propagation Through Structure (BPTS) algorithm. In the second step, an image with distinct object(s) is processed by an attention-driven retrieval strategy emphasizing distinct objects. On the other hand, an image without distinct object(s) (e.g., a scenery images) is processed by a fusing-all retrieval strategy. An improved performance can be obtained by using this combined approach. Copyright © 2010 Springer-Verlag Berlin Heidelberg.
|Title of host publication||Advanced concepts for intelligent vision systems: 12th International Conference, ACIVS 2010, Sydney, Australia, December 13-16, 2010, proceedings, part I|
|Editors||Jacques BLANC-TALON, Don BONE, Wilfried PHILIPS, Dan POPESCU, Paul SCHEUNDERS|
|Place of Publication||Berlin, Heidelberg|
|Publication status||Published - 2010|