NDLI: From dictionary of visual words to subspaces: Locality-constrained affine subspace coding

Content Provider	IEEE Xplore Digital Library
Author	Peihua Li Xiaoxiao Lu Qilong Wang
Copyright Year	2015
Description	Author affiliation: Sch. of Inf. & Commun. Eng., Dalian Univ. of Technol., Dalian, China (Peihua Li; Xiaoxiao Lu; Qilong Wang)
Abstract	The locality-constrained linear coding (LLC) is a very successful feature coding method in image classification. It makes known the importance of locality constraint which brings high efficiency and local smoothness of the codes. However, in the LLC method the geometry of feature space is described by an ensemble of representative points (visual words) while discarding the geometric structure immediately surrounding them. Such a dictionary only provides a crude, piecewise constant approximation of the data manifold. To approach this problem, we propose a novel feature coding method called locality-constrained affine subspace coding (LASC). The data manifold in LASC is characterized by an ensemble of subspaces attached to the representative points (or affine subspaces), which can provide a piecewise linear approximation of the manifold. Given an input descriptor, we find its top-k neighboring subspaces, in which the descriptor is linearly decomposed and weighted to form the first-order LASC vector. Inspired by the success of usage of higher-order information in image classification, we propose the second-order LASC vector based on the Fisher information metric for further performance improvement. We make experiments on challenging benchmarks and experiments have shown the LASC method is very competitive.
Starting Page	2348
Ending Page	2357
File Size	1668529
Page Count	10
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7298848
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Encoding Silicon Manifolds Dictionaries Visualization Measurement Piecewise linear approximation
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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