Sparse Non-negative Matrix Factorization Based on Spatial Pyramid Matching for Face Recognition

2013 ◽  
Author(s):  
Xianzhong Long ◽  
Hongtao Lu ◽  
Yong Peng
Keyword(s):  
Face Recognition ◽  
Matrix Factorization ◽  
Spatial Pyramid Matching ◽  
Pyramid Matching ◽  
Spatial Pyramid ◽  
Non Negative Matrix Factorization

scholarly journals Local-Tetra-Patterns for Face Recognition Encoded on Spatial Pyramid Matching

2022 ◽  
Vol 70 (3) ◽  
pp. 5039-5058
Author(s):  
Khuram Nawaz Khayam ◽  
Zahid Mehmood ◽  
Hassan Nazeer Chaudhry ◽  
Muhammad Usman Ashraf ◽  
Usman Tariq ◽  
...  
Keyword(s):  
Face Recognition ◽  
Spatial Pyramid Matching ◽  
Pyramid Matching ◽  
Spatial Pyramid

scholarly journals Weighted Spatial Pyramid Matching Collaborative Representation for Remote-Sensing-Image Scene Classification

2019 ◽  
Vol 11 (5) ◽  
pp. 518 ◽  
Author(s):  
Bao-Di Liu ◽  
Jie Meng ◽  
Wen-Yang Xie ◽  
Shuai Shao ◽  
Ye Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Remote Sensing Image ◽  
Superior Performance ◽  
Collaborative Representation ◽  
Scene Classification ◽  
Spatial Pyramid Matching ◽  
Classification Tasks ◽  
Pyramid Matching ◽  
Spatial Pyramid

At present, nonparametric subspace classifiers, such as collaborative representation-based classification (CRC) and sparse representation-based classification (SRC), are widely used in many pattern-classification and -recognition tasks. Meanwhile, the spatial pyramid matching (SPM) scheme, which considers spatial information in representing the image, is efficient for image classification. However, for SPM, the weights to evaluate the representation of different subregions are fixed. In this paper, we first introduce the spatial pyramid matching scheme to remote-sensing (RS)-image scene-classification tasks to improve performance. Then, we propose a weighted spatial pyramid matching collaborative-representation-based classification method, combining the CRC method with the weighted spatial pyramid matching scheme. The proposed method is capable of learning the weights of different subregions in representing an image. Finally, extensive experiments on several benchmark remote-sensing-image datasets were conducted and clearly demonstrate the superior performance of our proposed algorithm when compared with state-of-the-art approaches.

scholarly journals Spatial Pyramid Matching

2010 ◽  
pp. 401-415 ◽  
Author(s):  
Svetlana Lazebnik ◽  
Cordelia Schmid ◽  
Jean Ponce
Keyword(s):  
Spatial Pyramid Matching ◽  
Pyramid Matching ◽  
Spatial Pyramid

scholarly journals A New Scene Classification Method Based on Local Gabor Features

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Baoyu Dong ◽  
Guang Ren
Keyword(s):  
Classification Method ◽  
Scene Classification ◽  
Spatial Pyramid Matching ◽  
Visual Words ◽  
Feature Vectors ◽  
Feature Descriptors ◽  
Gabor Features ◽  
Gabor Feature ◽  
Pyramid Matching ◽  
Spatial Pyramid

A new scene classification method is proposed based on the combination of local Gabor features with a spatial pyramid matching model. First, new local Gabor feature descriptors are extracted from dense sampling patches of scene images. These local feature descriptors are embedded into a bag-of-visual-words (BOVW) model, which is combined with a spatial pyramid matching framework. The new local Gabor feature descriptors have sufficient discrimination abilities for dense regions of scene images. Then the efficient feature vectors of scene images can be obtained byK-means clustering method and visual word statistics. Second, in order to decrease classification time and improve accuracy, an improved kernel principal component analysis (KPCA) method is applied to reduce the dimensionality of pyramid histogram of visual words (PHOW). The principal components with the bigger interclass separability are retained in feature vectors, which are used for scene classification by the linear support vector machine (SVM) method. The proposed method is evaluated on three commonly used scene datasets. Experimental results demonstrate the effectiveness of the method.

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