International Journal of Computer Vision
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
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
Cutting-plane training of structural SVMs
Machine Learning
CENTRIST: A Visual Descriptor for Scene Categorization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histogram of Oriented Uniform Patterns for robust place recognition and categorization
International Journal of Robotics Research
Entropy rate superpixel segmentation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Beyond spatial pyramids: Receptive field learning for pooled image features
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Object-Centric spatial pooling for image classification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
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This paper presents a novel structural model based scene recognition method. In order to resolve regular grid image division methods which cause low content discriminability for scene recognition in previous methods, we partition an image into a pre-defined set of regions by superpixel segmentation. And then classification is modelled by introducing a structural model which has the capability of organizing unordered features of image patches. In the implementation, CENTRIST which is robust to scene recognition is used as original image feature, and bag-of-words representation is used to capture the local appearances of an image. In addition, we incorporate adjacent superpixel's differences as edge features. Our models are trained using structural SVM. Two state-of-the-art scene datasets are adopted to evaluate the proposed method. The experiment results show that the recognition accuracy is significantly improved by the proposed method.