The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing images using color coherence vectors
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Independent component analysis: algorithms and applications
Neural Networks
Applications of Video-Content Analysis and Retrieval
IEEE MultiMedia
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
An introduction to variable and feature selection
The Journal of Machine Learning Research
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Statistical Approach to Texture Classification from Single Images
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Eigenregions for Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image region entropy: a measure of "visualness" of web images associated with one concept
Proceedings of the 13th annual ACM international conference on Multimedia
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
Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Semantic Modeling of Natural Scenes for Content-Based Image Retrieval
International Journal of Computer Vision
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Knowledge and Information Systems
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Forward semi-supervised feature selection
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Efficient object category recognition using classemes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Towards a Relevant and Diverse Search of Social Images
IEEE Transactions on Multimedia
Assistive tagging: A survey of multimedia tagging with human-computer joint exploration
ACM Computing Surveys (CSUR)
Complex Object Correspondence Construction in Two-Dimensional Animation
IEEE Transactions on Image Processing
Improving Image Classification Using Semantic Attributes
International Journal of Computer Vision
Pairwise constraints based multiview features fusion for scene classification
Pattern Recognition
Indoor scene recognition by a mobile robot through adaptive object detection
Robotics and Autonomous Systems
Modern Machine Learning Techniques and Their Applications in Cartoon Animation Research
Modern Machine Learning Techniques and Their Applications in Cartoon Animation Research
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Scene classification is a challenging problem in computer vision. Though conventional methods show good performance in recognizing outdoor scenes, these methods does not work well in indoor scenes recognition. In recent years, high level image representations consisted of semantic attribute information has been introduced to solve this problem. However, a key technical challenge for these representations is the ''curse of dimensionality'', caused by the large numbers of objects and high dimensionality of the response vector for each object. In this paper, we propose a hypergraph learning algorithm based feature selection method for indoor scene classification. It performs feature selection by hypergraph regularization, which not only considers the interaction among features but also the interaction between the feature selection heuristics and the corresponding classifier. For the convenience of the prediction of the new images, a liner regression model is integrated in the framework, making the new images classification directly and in real time. The experimental results show that our approach has satisfactory performance compared with previously proposed methods.