Improved spatial pyramid matching for scene recognition

Speeded Up Robust Features ◽

Indoor Scene ◽

Visual Descriptors ◽

Pyramid Matching ◽

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.

2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics ◽

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

10.1109/ihmsc.2013.27 ◽

2013 ◽

Cited By ~ 1

Author(s):

Xianzhong Long ◽

Hongtao Lu ◽

Yong Peng

Keyword(s):

Face Recognition ◽

Matrix Factorization ◽

Pyramid Matching ◽

Spatial Pyramid ◽

Non Negative Matrix Factorization

An image classification algorithm integrating principal component analysis and spatial pyramid matching features

2017 Fourth International Conference on Image Information Processing (ICIIP) ◽

10.1109/iciip.2017.8313779 ◽

2017 ◽

Cited By ~ 1

Author(s):

Qingyan Li ◽

Jinye Peng ◽

Zhan Li ◽

Yifeng Ren

Keyword(s):

Principal Component Analysis ◽

Image Classification ◽

Principal Component ◽

Component Analysis ◽

Classification Algorithm ◽

Pyramid Matching ◽

Siamese Spatial Pyramid Matching Network with Location Prior for Anatomical Landmark Tracking in 3-Dimension Ultrasound Sequence

Pattern Recognition and Computer Vision - Lecture Notes in Computer Science ◽

10.1007/978-3-030-31723-2_29 ◽

2019 ◽

pp. 341-353

Author(s):

Jishuai He ◽

Chunxu Shen ◽

Yibin Huang ◽

Jian Wu

Keyword(s):

Anatomical Landmark ◽

Matching Network ◽

Landmark Tracking ◽

Pyramid Matching ◽

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

Remote Sensing ◽

10.3390/rs11050518 ◽

2019 ◽

Vol 11 (5) ◽

pp. 518 ◽

Cited By ~ 13

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 ◽

Classification Tasks ◽

Pyramid Matching ◽

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.

Spatial Pyramid Matching

Object Categorization ◽

10.1017/cbo9780511635465.022 ◽

2010 ◽

pp. 401-415 ◽

Cited By ~ 17

Author(s):

Svetlana Lazebnik ◽

Cordelia Schmid ◽

Jean Ponce

Keyword(s):

Pyramid Matching ◽

A New Scene Classification Method Based on Local Gabor Features

Mathematical Problems in Engineering ◽

10.1155/2015/109718 ◽

2015 ◽

Vol 2015 ◽

pp. 1-14 ◽

Cited By ~ 2

Author(s):

Baoyu Dong ◽

Guang Ren

Keyword(s):

Classification Method ◽

Scene Classification ◽

Visual Words ◽

Feature Vectors ◽

Feature Descriptors ◽

Gabor Features ◽

Gabor Feature ◽

Pyramid Matching ◽

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.