A new approach to the detection of moving objects
Information Sciences: an International Journal
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Geometric Context from a Single Image
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Localization Based on Building Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
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
A rough margin based support vector machine
Information Sciences: an International Journal
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of multiple configurations of objects with limited data
Pattern Recognition
A ν-twin support vector machine (ν-TSVM) classifier and its geometric algorithms
Information Sciences: an International Journal
Image clustering using local discriminant models and global integration
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Avoiding confusing features in place recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Scale-invariant shape features for recognition of object categories
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A lattice-based neuro-computing methodology for real-time human action recognition
Information Sciences: an International Journal
Personalized mode transductive spanning SVM classification tree
Information Sciences: an International Journal
Max-Min Distance Analysis by Using Sequential SDP Relaxation for Dimension Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Image-based facial sketch-to-photo synthesis via online coupled dictionary learning
Information Sciences: an International Journal
Learning image representations from the pixel level via hierarchical sparse coding
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback
IEEE Transactions on Pattern Analysis and Machine Intelligence
Complex Object Correspondence Construction in Two-Dimensional Animation
IEEE Transactions on Image Processing
Web and Personal Image Annotation by Mining Label Correlation With Relaxed Visual Graph Embedding
IEEE Transactions on Image Processing
A reduced support vector machine approach for interval regression analysis
Information Sciences: an International Journal
CVPR '13 Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition
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In this work, we propose a novel architecture style recognition model by introducing blocklets that capture the morphological characteristics of buildings. First, we decompose a building image into a collection of blocks, each representing a basic architecture component such as a stone pillar. To exploit the spatial correlations among blocks, we obtain locklets by extracting spatially adjacent blocks, and further formulate architecture style recognition as matching between blocklets extracted from different buildings. Toward an efficient blocklet-to-blocklet matching, a hierarchical sparse coding algorithm is proposed to represent each blocklet by a linear combination of basis blocklets. On the other hand, toward an effective matching process, an LDA [25,1]-like scheme is adopted to select the blocklets with high discrimination. Finally, we carry out architecture style recognition based on the selected highly discriminative blocklets. Experimental results on our own compiled data set demonstrate that the proposed approach outperforms several state-of-the-art place/building recognition models.