Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contextual Priming for Object Detection
International Journal of Computer Vision
Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Eigenregions for Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantic Modeling of Natural Scenes for Content-Based Image Retrieval
International Journal of Computer Vision
International Journal of Computer Vision
Review: Which is the best way to organize/classify images by content?
Image and Vision Computing
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Description of interest regions with local binary patterns
Pattern Recognition
Spatial Hierarchy of Textons Distributions for Scene Classification
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
Scene categorization via contextual visual words
Pattern Recognition
Building compact local pairwise codebook with joint feature space clustering
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Kernel sparse representation for image classification and face recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
CENTRIST: A Visual Descriptor for Scene Categorization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Objects as attributes for scene classification
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
Human-inspired features for natural scene classification
Pattern Recognition Letters
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This paper presents a simple but effective scene classification approach based on the incorporation of a multi-resolution representation into a bag-of-features model. In the proposed approach, we construct multiple resolution images and extract local features from all the resolution images with dense regions. We then quantize these extracted features into a visual codebook using the k-means clustering method. To incorporate spatial information, two modalities of horizontal and vertical partitions are adopted to partition all resolution images into sub-regions with different scales. Each sub-region is then represented as a histogram of codeword occurrences by mapping the local features to the codebook. The proposed approach is evaluated over five commonly used data sets including indoor scenes, outdoor scenes, and sports events. The experimental results show that the proposed approach performs competitively against previous methods across all data sets. Furthermore, for the 8 scenes, 13 scenes, 67 indoor scenes, and 8 sport events data sets, the proposed approach outperforms state-of-the-art methods.