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Author Zhi-Hua Zhou ♦ Min-Ling Zhang ♦ Ke-Jia Chen
Sponsorship IEEE Comput. Soc
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2003
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Image generation ♦ Image databases ♦ Information retrieval ♦ Image retrieval ♦ Pixel ♦ Merging ♦ Scattering ♦ Laboratories ♦ Content based retrieval ♦ Image converters
Abstract In multi-instance learning, the training examples are bags composed of instances without labels and the task is to predict the labels of unseen bags through analyzing the training bags with known labels. In content-based image retrieval (CBIR), the query is ambiguous because it is hard to ask the user precisely specify what he or she wants. Such kind of ambiguity can be gracefully dealt with by multi-instance learning techniques, and previous research shows that bag generators can significantly influence the performance of a CBIR system. In this paper, a novel bag generator named ImaBag is presented, where the pixels of each image are first clustered based on their color and spatial features and then the clustered blocks are merged and converted into a specific number of instances. Experiments show that ImaBag achieves comparable results to some existing bag generators but is more efficient in retrieving images from databases.
Description Author affiliation: National Lab. for Novel Software Technol., Nanjing Univ., China (Zhi-Hua Zhou; Min-Ling Zhang; Ke-Jia Chen)
ISBN 0769520383
ISSN 10823409
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2003-11-05
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 315.73 kB
Page Count 5
Starting Page 565
Ending Page 569


Source: IEEE Xplore Digital Library