A New Scene Classification Method Based on Spatial Pyramid Matching Model

2015 ◽  
Vol 12 (3) ◽  
pp. 1073-1080
Author(s):  
Baoyu Dong
Keyword(s):  
Classification Method ◽  
Scene Classification ◽  
Matching Model ◽  
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.

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.

Indoor Scene recognition for Micro Aerial Vehicles Navigation using Enhanced SIFT-ScSPM Descriptors

2019 ◽  
Vol 73 (1) ◽  
pp. 37-55 ◽  
Author(s):  
B. Anbarasu ◽  
G. Anitha
Keyword(s):  
State Of The Art ◽  
Scene Recognition ◽  
Support Vector ◽  
Scene Classification ◽  
Spatial Pyramid Matching ◽  
Speeded Up Robust Features ◽  
Indoor Scene ◽  
Visual Descriptors ◽  
Pyramid Matching ◽  
Spatial Pyramid

In this paper, a new scene recognition visual descriptor called Enhanced Scale Invariant Feature Transform-based Sparse coding Spatial Pyramid Matching (Enhanced SIFT-ScSPM) descriptor is proposed by combining a Bag of Words (BOW)-based visual descriptor (SIFT-ScSPM) and Gist-based descriptors (Enhanced Gist-Enhanced multichannel Gist (Enhanced mGist)). Indoor scene classification is carried out by multi-class linear and non-linear Support Vector Machine (SVM) classifiers. Feature extraction methodology and critical review of several visual descriptors used for indoor scene recognition in terms of experimental perspectives have been discussed in this paper. An empirical study is conducted on the Massachusetts Institute of Technology (MIT) 67 indoor scene classification data set and assessed the classification accuracy of state-of-the-art visual descriptors and the proposed Enhanced mGist, Speeded Up Robust Features-Spatial Pyramid Matching (SURF-SPM) and Enhanced SIFT-ScSPM visual descriptors. Experimental results show that the proposed Enhanced SIFT-ScSPM visual descriptor performs better with higher classification rate, precision, recall and area under the Receiver Operating Characteristic (ROC) curve values with respect to the state-of-the-art and the proposed Enhanced mGist and SURF-SPM visual descriptors.

scholarly journals A Real-Time Infrared Ultra-Spectral Signature Classification Method via Spatial Pyramid Matching

Sensors ◽  
2015 ◽  
Vol 15 (7) ◽  
pp. 15868-15887 ◽  
Author(s):  
Xiaoguang Mei ◽  
Yong Ma ◽  
Chang Li ◽  
Fan Fan ◽  
Jun Huang ◽  
...  
Keyword(s):  
Real Time ◽  
Classification Method ◽  
Spectral Signature ◽  
Spatial Pyramid Matching ◽  
Pyramid Matching ◽  
Spatial Pyramid

scholarly journals A NOVEL SELF-TAUGHT LEARNING FRAMEWORK USING SPATIAL PYRAMID MATCHING FOR SCENE CLASSIFICATION

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 725-729
Author(s):  
Y. Yang ◽  
D. Zhu ◽  
F. Ren ◽  
C. Cheng
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Earth Observation ◽  
Data Sets ◽  
Scene Classification ◽  
Data Set ◽  
Spatial Pyramid Matching ◽  
Learning Framework ◽  
Pyramid Matching ◽  
Spatial Pyramid

Abstract. Remote sensing earth observation images have a wide range of applications in areas like urban planning, agriculture, environment monitoring, etc. While the industrial world benefits from availability of high resolution earth observation images since recent years, interpreting such images has become more challenging than ever. Among many machine learning based methods that have worked out successfully in remote sensing scene classification, spatial pyramid matching using sparse coding (ScSPM) is a classical model that has achieved promising classification accuracy on many benchmark data sets. ScSPM is a three-stage algorithm, composed of dictionary learning, sparse representation and classification. It is generally believed that in the dictionary learning stage, although unsupervised, one should use the same data set as classification stage to get good results. However, recent studies in transfer learning suggest that it might be a better strategy to train the dictionary on a larger data set different from the one to classify.In our work, we propose an algorithm that combines ScSPM with self-taught learning, a transfer learning framework that trains a dictionary on an unlabeled data set and uses it for multiple classification tasks. In the experiments, we learn the dictionary on Caltech-101 data set, and classify two remote sensing scene image data sets: UC Merced LandUse data set and Changping data set. Experimental results show that the classification accuracy of proposed method is compatible to that of ScSPM. Our work thus provides a new way to reduce resource cost in learning a remote sensing scene image classifier.

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