局部敏感字典学习提升图像分类的非线性表现

63 浏览量更新于2024-08-27 收藏 748KB PDF 举报

本文探讨了位置敏感字典学习在图像分类领域的潜在优势，特别是在利用稀疏表示进行高效图像特征表示以及增强非线性特性对提升分类性能的作用。传统基于字典学习的方法虽然在图像分类任务上表现出色，但其线性特性可能限制了对复杂非线性结构的捕捉。为了解决这个问题，研究者提出了一个创新的局部敏感字典学习算法，旨在保持全局一致性的同时引入局部敏感性，以降低对线性的依赖，同时在保持计算成本相对较低的前提下提高模型的灵活性。在该算法中，关键步骤是将图像特征分解成若干个局部敏感的子集，每个子集对应一个局部字典。通过这种方法，算法能够更好地适应图像的不同区域，捕捉到不同区域内的独特纹理和模式。局部敏感性有助于减少特征之间的冗余，使得字典学习更能反映图像的空间结构。此外，作者还加入了全局一致性正则化器，这有助于确保整个字典的一致性和整体模型的稳定性，防止局部最优导致的性能下降。实验部分，作者在多个基准数据集上进行了详细的评估，包括但不限于MNIST手写数字、CIFAR-10和ImageNet等知名图像分类任务。结果显示，局部敏感字典学习算法相较于传统的字典学习方法，在保持较高识别精度的同时，显著提高了对非线性图像特征的建模能力，从而在实际图像分类任务中取得了更为优秀的性能。总结来说，这篇论文的主要贡献在于提出了一种结合局部敏感性和全局一致性约束的字典学习策略，有效地解决了图像分类中的非线性表示问题。这种新型方法在实际应用中展现了强大的性能和广泛的应用潜力，为图像处理和机器学习领域提供了新的研究方向和实践工具。

LOCALITY SENSITIVE DICTIONARY LEARNING FOR IMAGE CLASSIFICATION

Bao-Di Liu, Bin Shen*, Xue Li

College of Information and Control Engineering China University of Petroleum Qingdao, 266580, China

Google Research, New York, USA

Department of Electronic Engineering Tsinghua University Beijing, 100084, China

thu.liubaodi@gmail.com, bshen@google.com, xue-li11@mails.tsinghua.edu.cn

ABSTRACT

In this paper, motivated by the superior performance of sparse rep-

resentation based dictionary learning for application of image clas-

siﬁcation and the usage of nonlinearity property in improving per-

formance of image representation, we propose a locality sensitive

dictionary learning algorithm with global consistency and smooth-

ness constraint to overcome the restriction of linearity at relatively

low cost. Speciﬁcally, the image features are partitioned into several

groups in a locality sensitive way and a global consistency regular-

izer is embedded into locality sensitive dictionary learning algorith-

m. The proposed algorithm is efﬁcient to capture complex nonlin-

ear structure. Experimental results on several benchmark data sets

demonstrate the efﬁciency of our proposed locality sensitive dictio-

nary learning algorithm.

Index Terms— Dictionary Learning, Sparse Representation,

Locality Sensitive, Image Classiﬁcation

1. INTRODUCTION

Image classiﬁcation task, which aims at automatically associate im-

ages with semantic labels, has become quite a signiﬁcant topic in

computer vision area. The most common framework for image clas-

siﬁcation is the discriminative model [1, 2, 3, 4, 5]. Five main step-

s, include image feature extraction, dictionary learning, image fea-

ture coding, image pooling, and SVM-based classiﬁcation, construct

the discriminative model [1], where dictionary learning plays a key

role. A dictionary is usually composed of visual words, which en-

code low level visual information of images across different classes.

The primitive versions of vocabulary are learnt by typically k-means

clustering [6]. Yang et al. [2] ﬁrst introduced sparse representa-

tion based dictionary learning algorithm and obtained state-of-the-

art performance in image classiﬁcation.

On the other hand, various sparse representation based dictio-

nary learning algorithms were emerged. And sparse representation

based dictionary learning technique has been gradually revealed in

computer vision areas, such as image classiﬁcation[7, 8, 9], im-

age inpainting [10, 11], image resolution, face recognition [12, 13]

etc. Different from other factorization methods, such as PCA, non-

negative matrix factorization [14, 15, 16, 17, 18, 19, 20], tensor fac-

This work was done when Bin was with Department of Computer Sci-

ence, Purdue University, West Lafayette.

This work was supported by the National Natural Science Foundation

of China (Grant No.61402535 No.61271407), the Natural Science Founda-

tion for Youths of Shandong Province, China (Grant No.ZR2014FQ001),

Qingdao Science and Technology Project (No.14-2-4-111-jch), and the Fun-

damental Research Funds for the Central Universities, China University of

Petroleum (East China) (Grant No. 14CX02169A).

Fig. 1. Framework of the proposed LSDL. The data matrix X is

partitioned into C clusters, then a dictionary is learnt for each region.

The dictionary W learnt for the data matrix X is the smoothness and

consistency of the local dictionaries learnt for each region.

torization [21, 22, 23, 24] and low-rank factorization [25, 26], sparse

representation based dictionary learning has the ability to represent

data by sparse linear combination of bases. Recently, more and more

researchers focused on locality-preserving dictionary learning algo-

rithms due that locality was more essential than sparsity [27]. Wang

et al. [3] considered each word in the vocabulary lying on a man-

ifold to preserve the local information. Wei et al. [28] embedded

the local data structure into sparse representation based dictionary

learning algorithm. Gao et al. [29] incorporated the histogram in-

tersection kernel based laplacian matrix into the sparse representa-

tion based dictionary learning algorithm to enforce the consistence

in sparse representation of similar local features. Zheng et al. [30]

explicitly embedded the vector quantization based laplancian matrix

into dictionary learning algorithm. Liu et al. [5] proposed sparse

representation in k-nearest neighbor to construct the graph model to

improve the accuracy and robustness of image representation.

However, all these methods are restricted to linear dictionary

learning methods or graph embedded dictionary learning methods,

and thus unable to capture complex nonlinear properties. Many real

word data require complex nonlinearity in dictionary learning due to

their distribution. For example, handwritten digits form manifolds

or human face form manifolds in the feature space is hard to cap-

ture such complex nonlinearity structure by conventional dictionary

learning methods, especially when facing large data. Moreover, k-

ernel trick needs to apply the kernel function to all pairs of sam-

ples which cost a lot of computation. To improve the nonlinearity

and to keep the computational efﬁciency, a locality sensitive dictio-

nary learning (LSDL) method is proposed to approximate the global

nonlinear dictionary to capture complex correlation structures. The

assumption is that the global dictionary learning is nonlinear, but it

is linear locally, i.e. dictionary learning method is applicable when

3807

ICIP 2015

下载后可阅读完整内容，剩余4页未读，立即下载

weixin_38592332

粉丝: 7
资源: 888

局部敏感字典学习提升图像分类的非线性表现

matlab_k-svd算法用于稀疏表示的图像去噪，字典学习算法

用于图像分类的极限学习机和自适应稀疏表示

OCR图像识别

通过稀疏表示使用多个字典进行超分辨率映射

基于类别特定字典的显著性编码图像分类

创新软分配位置-方向池化提升图像分类性能

低秩判别投影字典对学习：提升模式识别效率与鲁棒性

深度学习技术实现大规模图像检索系统

Python实现KNN分类算法的深度学习实例解析

图像识别中的迁移学习

最新资源