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Spectral Regression Subspace learning Machine learning Image classification (2014)
BibTeX
@MISC{N14spectralregression,
author = {Liyuan Li N and Weixun Goh and Joo Hwee Lim and Sinno Jialin Pan and Scene Recognition},
title = {Spectral Regression Subspace learning Machine learning Image classification},
year = {2014}
}
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Abstract
Computer vision a b s t r a c t This paper proposes a novel method based on Spectral Regression (SR) for efficient scene recognition. First, a new SR approach, called Extended Spectral Regression (ESR), is proposed to perform manifold learning on a huge number of data samples. Then, an efficient Bag-of-Words (BOW) based method is developed which employs ESR to encapsulate local visual features with their semantic, spatial, scale, and orientation information for scene recognition. In many applications, such as image classification and multimedia analysis, there are a huge number of low-level feature samples in a training set. It prohibits direct application of SR to perform manifold learning on such dataset. In ESR, we first group the samples into tiny clusters, and then devise an approach to reduce the size of the similarity matrix for graph learning. In this way, the subspace learning on graph Laplacian for a vast dataset is computationally feasible on a personal computer. In the ESR-based scene recognition, we first propose an enhanced low-level feature representation which combines the scale, orientation, spatial position, and local appearance of a local feature. Then, ESR is applied to embed enhanced low-level image features. The ESR-based feature embedding not only generates a low dimension feature representation but also integrates various aspects of low-level features into the compact representation. The bag-of-words is then generated from the embedded features for image classification. The comparative experiments on open benchmark datasets for scene recognition demonstrate that the proposed method outperforms baseline approaches. It is suitable for real-time applications on mobile platforms, e.g. tablets and smart phones. & 2014 Elsevier Ltd. All rights reserved. 1.
Keyphrases
image classification spectral regression subspace huge number local visual feature spatial position similarity matrix real-time application esr-based scene recognition efficient scene recognition mobile platform training set enhanced low-level feature representation compact representation data sample graph learning local feature low dimension feature representation vast dataset local appearance spectral regression baseline approach various aspect graph laplacian embedded feature comparative experiment extended spectral regression new sr approach direct application open benchmark datasets orientation information enhanced low-level image feature multimedia analysis smart phone computer vision tiny cluster low-level feature sample scene recognition demonstrate many application novel method low-level feature efficient bag-of-words esr-based feature elsevier ltd personal computer scene recognition
