Classification of scene photographs from local orientations features
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Upgrading Color Distributions for Image Retrieval: Can We Do Better?
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
Scene-Centered Description from Spatial Envelope Properties
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated 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)
Boosting Image Orientation Detection with Indoor vs. Outdoor Classification
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
A Computationally Efficient Approach to Indoor/Outdoor Scene Classification
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Experimental result analysis for a generative probabilistic image retrieval model
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Distinguishing paintings from photographs
Computer Vision and Image Understanding
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
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Category-Level Object Recognition (Lecture Notes in Computer Science)
Toward Category-Level Object Recognition (Lecture Notes in Computer Science)
Efficient object-class recognition by boosting contextual information
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Automatic image orientation detection
IEEE Transactions on Image Processing
Impediments to general purpose Content Based Image search
C3S2E '09 Proceedings of the 2nd Canadian Conference on Computer Science and Software Engineering
Dense sampling low-level statistics of local features
Proceedings of the ACM International Conference on Image and Video Retrieval
Visual word pairs for automatic image annotation
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Mixture model based contextual image retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
Automatic image search based on improved feature descriptors and decision tree
Integrated Computer-Aided Engineering
A review on automatic image annotation techniques
Pattern Recognition
Rotation Invariant Curvelet Features for Region Based Image Retrieval
International Journal of Computer Vision
Apples to oranges: evaluating image annotations from natural language processing systems
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Structural image retrieval using automatic image annotation and region based inverted file
Journal of Visual Communication and Image Representation
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This paper describes an efficient approach to image annotation. It ranked first on the recent scene categorization track of the ImagEVAL1 benchmark. We show how homogeneous global image descriptors combined with a pool of Support Vector Machines achieve very good results. We also used this approach on several well known object recognition databases to emphasize two main aspects of this research domain: the importance of contextual information in object recognition and the unsuitability of many standard databases for this task.