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Multimedia Systems
Erschienen in: Multimedia Systems 2/2015

01.03.2015 | Special Issue Paper

A new discriminative coding method for image classification

verfasst von: Xiaoshan Yang, Tianzhu Zhang, Changsheng Xu

Erschienen in: Multimedia Systems | Ausgabe 2/2015

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Abstract

The bag-of-words (BOW) based methods are widely used in image classification. However, huge number of visual information is omitted inevitably in the quantization step of the BOW. Recently, NBNN and its improved methods like Local NBNN were proposed to solve this problem. Nevertheless, these methods do not perform better than the state-of-the-art BOW based methods. In this paper, based on the advantages of BOW and Local NBNN, we introduce a novel locality discriminative coding (LDC) method. We convert each low level local feature, such as SIFT, into code vector using the Local Feature-to-Class distance other than by k-means quantization. After coding, sum-pooling combined with SPM is used to construct a single feature representation vector for each image. Extensive experimental results on several challenging benchmark datasets show that our LDC method outperforms six state-of-the-art image classification methods.
Vorheriger Artikel Guest editorial: selected papers from ICIMCS 2013
Nächster Artikel Large-margin multi-view Gaussian process

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Metadaten
Titel
A new discriminative coding method for image classification
verfasst von
Xiaoshan Yang
Tianzhu Zhang
Changsheng Xu
Publikationsdatum
01.03.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Multimedia Systems / Ausgabe 2/2015
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-014-0376-y

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