.png){kind=icon}
.png){kind=icon}
Return
A genetic programming framework for content-based image retrieval
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2008.04.010.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The effectiveness of content-based image retrieval (CBIR) systems can be improved by combining image features or by weighting image similarities, as computed from multiple feature vectors. However, feature combination do not make sense always and the combined similarity function can be more complex than weight-based functions to better satisfy the users' expectations. We address this problem by presenting a Genetic Programming framework to the design of combined similarity functions, Our method allows nonlinear combination of image similarities and is validated through several experiments, where the images are retrieved based on the shape of their objects. Experimental results demonstrate that the GP framework is suitable for the design of effective combinations functions. (C) 2008 Elsevier Ltd. All rights reserved.
Keywords:
Content-based image retrieval
Genetic programming
Shape descriptors
Image analysis
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W