NDLI: Multiple kernel learning with ICA: Local discriminative image descriptors for recognition

Content Provider	IEEE Xplore Digital Library
Author	Si-Yao Fu Guo-Sheng Yang Zeng-Guang Hou
Copyright Year	2010
Description	Author affiliation: Key laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, P.O. Box 2728, Beijing 100190, China (Zeng-Guang Hou) \|\| School of Information and Engineering, the Central University of Nationalities, Beijing 100081, China (Si-Yao Fu; Guo-Sheng Yang)
Abstract	Local image features have been proven to be a powerful way to describe pattern of interest, both from single objects and complex scenes. While learning from images represented by local features is challenging, recent publications and developments in object recognition has shown that significant performance achievements can be achieved by carefully combining multi-level, coarse-to-fine, sparsely distributed feature encodings [10], and kernel based learning methods, which defines a generalized similarity measure among data using multiple kernel functions instead of a single one, also known as multiple kernel learning (MKL). In this paper we show that the Kernel ICA descriptors based MKL supervised learning approach perform better than other descriptors for object recognition, since the ICA-based representation is localized. In low-level feature extraction, ICA produces independent image bases that emphasize edge information in the image data. In high-level classification, MKL classifies the ICA features as discriminative components. We demonstrate our algorithm on different databases for recognition tasks, showing that the proposed method is accurate and more efficient than current approaches.
Starting Page	1
Ending Page	6
File Size	721756
Page Count	6
File Format	PDF
ISBN	9781424469161
ISSN	10987576
e-ISBN	9781424469185
DOI	10.1109/IJCNN.2010.5596572
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-18
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Support vector machines Databases Training Face Feature extraction Principal component analysis
Content Type	Text
Resource Type	Article

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