SIFT-WCS-LTP特征：稀疏编码与空间金字塔匹配在图像分类中的应用

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"这篇研究论文探讨了一种名为SIFT-WCS-LTP特征的空间金字塔匹配表示在图像分类中的应用，利用稀疏编码技术提高效率。该方法由Mingming Huang、Zhichun Mu和Hui Zeng共同提出，发表于《IET Image Processing》期刊上。" 文章介绍了图像分类系统中形状和纹理信息的重要性。为了更准确地捕捉这些信息，作者们提出了一种新的描述符——加权中心对称局部 ternary模式（Weighted Centre-Symmetric Local Ternary Pattern，WCS-LTP）。WCS-LTP能够更好地表征图像的局部纹理特性，相比传统的局部CS-LTP和SIFT特征，它能更有效地捕获图像的形状信息，并且在提取纹理信息时更为精确。接着，基于这个创新的WCS-LTP描述符，作者们引入了一种新的局部尺度不变特征变换方法，即SIFT-WCS-LTP特征提取方法。SIFT-WCS-LTP结合了SIFT（Scale-Invariant Feature Transform）的尺度不变性与WCS-LTP的纹理描述能力，形成一种强大的特征表示。为了提高图像分类的效率，文章还采用了稀疏编码的策略。稀疏编码是一种表示方法，它将高维特征向量转换为一组简化的线性组合，即稀疏码字，这有助于减少计算复杂性和存储需求，同时保持特征的关键信息。在SIFT-WCS-LTP特征空间中应用这种稀疏编码的匹配表示，可以增强不同图像之间的相似性度量，从而改善分类性能。在空间金字塔匹配（Spatial Pyramid Matching，SPM）框架下，图像被分成多个层次的区域，每个区域内的特征被分别编码并匹配。这种方法考虑了图像的局部上下文，提高了匹配的稳健性，尤其在处理图像的尺度和旋转变化时。通过对各种基准数据集的实验验证，SIFT-WCS-LTP特征空间金字塔匹配表示显示出了优秀的分类性能，证明了其在图像分类任务中的优越性。这些实验结果进一步支持了作者们的观点，即结合形状、纹理和稀疏编码的特征提取方法可以显著提升图像分类系统的准确性和效率。这篇研究论文提出了一种新颖的图像特征表示方法，即SIFT-WCS-LTP，结合了稀疏编码和空间金字塔匹配，旨在优化图像分类的性能。这种方法为计算机视觉领域的图像分析和识别提供了一个有价值的工具，特别是在处理复杂和多变的视觉场景时。

Efficient image classification via sparse

coding spatial pyramid matching

representation of SIFT-WCS-LTP feature

ISSN 1751-9659

Received on 24th November 2014

Revised on 14th July 2015

Accepted on 21st July 2015

doi: 10.1049/iet-ipr.2015.0329

www.ietdl.org

Mingming Huang, Zhichun Mu

✉

, Hui Zeng

School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083,

People’s Republic of China

✉ E-mail: mu@ies.ustb.edu.cn

Abstract: Shape and texture information are critical to the accuracy of image classification systems. In this study, the

authors propose a novel descriptor called weighted centre-symmetric local ternary pattern (WCS-LTP), better

characterising the image local texture. Then, based on the proposed WCS-LTP descriptor, they introduce a new local

scale invariant feature transform and WCS-LTP (SIFT–WCS-LTP) feature extractio n approach. Compared with

conventional local CS-LTP and SIFT features, the authors’ proposed SIFT–WCS-LTP feature can not only capture the

shape information of images, but also tend to extract more precise texture information. Finally, SIFT–WCS-LTP fe ature-

based sparse coding spatial pyramid matching (ScSPM) representation classification is proposed for image

classification. Extensive experimental results demonstrate that the effectiveness of their proposed SIFT–WCS-LTP

feature-based ScSPM representation classification algorithm.

1 Introduction

Image classiﬁcation, which annotates an image with one or multiple

labels corresponding to different semantic classes, is an important

research topic in the areas of computer vision, pattern recognition,

and machine learning. Moreover, image classiﬁcation has attracted

an increasing amount of attention over the past few years, because

of its wide use in a broad range of applications such as human–

computer interaction [1], video surveillance [2] and robot path

planning [3].

Standard image classiﬁcation pipelines use features (descriptors)

in combination with classiﬁers [4–6]. For good classiﬁcation,

features should be descriptive and discriminative, and on the other

hand, invariant to different transformations and robust enough to

allow intra-class variation. In recent years, much effort has been

invested in developing features that yield good classiﬁcation and

the focus in extracting features for classiﬁcation has shifted from

global features describing the object as a whole, to local features.

Famous contributions include SIFT (scale invariant feature

transform) [7], (principal component analysis (PCA) and SIFT

(PCA–SIFT) [8], SURF (speeded-up robust features) [9] and so

on. Among them, the SIFT descriptor, proposed over a decade

ago, is currently among the best quality descriptors for image

classiﬁcation. It relies on a three-dimensional histogram of

gradient locations and orientations where the contribution to bins

is weighted by the gradient magnitude and a Gaussian window

overlaid over the region. Inspired by the high discriminative power

and robustness of SIFT, many researchers have developed varieties

of local descriptors following the way of SIFT. The PCA–SIFT

descriptor is an extension of the SIFT descriptor, which applies

PCA to reduce the dimensionality of the SIFT descriptor vector

from 128 to 36. The SURF descriptor also relies on local gradient

histograms and speeds up the gradient computations using integral

images, while almost preserving the quality of SIFT.

To better take advantage of local features, the bag-of-visual-words

(BoV) model [10], which has been very popular, is used in image

classiﬁcation. The BoV method represents an image as an

orderless collection of local features and its descriptive ability is

severely limited due to discarding the spatial information of

features. By overcoming this problem, one popular extension of

the BoV method, called the spatial pyramid matching (SPM) [11],

is proposed and has been shown to be effective for image

classiﬁcation. The SPM partitions an image into several segments

in different scales, then computes the BoV histogram within each

segment and concatenates all the histograms to form a high

dimension vector representation of the image. For the purpose of

reducing the training complexity and improving the scalability,

sparse coding SPM (ScSPM) method [12] taking into account

some aspects of the spatial layout of the image is proposed, which

contribute to improving classiﬁcation performance. Csurka et al.

[13] proposed BoV-based method for image classiﬁcation. The

proposed method was based on BoV model, where a set of SIFT

features is ﬁrst extracted and then an image is represented by the

BoV frequency histogram of SIFT features for image classiﬁcation.

Wang et al. [14] developed a new method of image classiﬁcation

by using the histogram of oriented gradient features which is

computed on a dense grid of uniformly spaced cells. In addition,

Akata et al. [15] applied PCA to reduce the dimensionality of the

SIFT descriptor from 128 to 64 for image classiﬁcation.

In modern days, the images on the website or computers normally

contain complex background. Although local features have been

proven to be very effective in image classiﬁcation, the accuracy of

classiﬁcation is often limited by the presence of uninformative

local features that typically extracted from background [16]. The

SIFT feature is capable of capturing local object shape or edge

with the distributions of intensity gradients. For an image with

simple background, the SIFT feature is able to accurately represent

the foreground objects without noise interference [17]. However, it

will perform poorly when the image contains complex background

due to the fact that a portion of extracted features may come from

the noisy background. On the contrary, the CS-LTP

(centre-symmetric local ternary pattern) descriptor [18] capturing

the texture information of images does not take into account shape

information in images. Furthermore, it can ﬁlter out background

noise through local ternary patterns. Therefore, effective local

feature extraction approaches, which could capture shape and

texture information, are still needed to be investigated for image

classiﬁcation.

This paper investigates an effective algorithm based on ScSPM

representation of scale invariant feature transform and WCS-LTP

(SIFT–WCS-LTP) feature for image classiﬁcation. Our feature

extraction scheme is ﬁrst to construct a novel descriptor called

IET Image Processing

Research Article

IET Image Process., 2016, Vol. 10, Iss. 1, pp. 61–67

The Institution of Engineering and Technology 2016

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