NDLI: Comparing data-dependent and data-independent embeddings for classification and ranking of Internet images

Content Provider	IEEE Xplore Digital Library
Author	Yunchao Gong Lazebnik, S.
Copyright Year	2011
Description	Author affiliation: Department of Computer Science, UNC Chapel Hill, NC, 27599 (Yunchao Gong; Lazebnik, S.)
Abstract	This paper presents a comparative evaluation of feature embeddings for classification and ranking in large-scale Internet image datasets. We follow a popular framework for scalable visual learning, in which the data is first transformed by a nonlinear embedding and then an efficient linear classifier is trained in the resulting space. Our study includes data-dependent embeddings inspired by the semi-supervised learning literature, and data-independent ones based on approximating specific kernels (such as the Gaussian kernel for GIST features and the histogram intersection kernel for bags of words). Perhaps surprisingly, we find that data-dependent embeddings, despite being computed from large amounts of unlabeled data, do not have any advantage over data-independent ones in the regime of scarce labeled data. On the other hand, we find that several data-dependent embeddings are competitive with popular data-independent choices for large-scale classification.
Starting Page	2633
Ending Page	2640
File Size	432763
Page Count	8
File Format	PDF
ISBN	9781457703942
ISSN	10636919
e-ISBN	9781457703959
DOI	10.1109/CVPR.2011.5995619
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Support vector machines Training Histograms Laplace equations Internet Training data
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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