Computer and Robot Vision
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
Proceedings of the 15th international conference on Multimedia
The Photographer's Eye: Composition and Design for Better Digital Photos
The Photographer's Eye: Composition and Design for Better Digital Photos
Saliency-enhanced image aesthetics class prediction
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Affective image classification using features inspired by psychology and art theory
Proceedings of the international conference on Multimedia
Aesthetic Image Classification for Autonomous Agents
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Saliency moments for image categorization
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Studying aesthetics in photographic images using a computational approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Towards category-based aesthetic models of photographs
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
High level describable attributes for predicting aesthetics and interestingness
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Conceptualizing Birkhoff's aesthetic measure using Shannon entropy and Kolmogorov complexity
Computational Aesthetics'07 Proceedings of the Third Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
Semantic indexing and computational aesthetics: interactions, bridgesand boundaries
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
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Scene recognition systems are generally based on features that represent the image semantics by modeling the content depicted in a given image. In this paper we propose a framework for scene recognition that goes beyond the mere visual content analysis by exploiting a new cue for categorization: the image composition, namely its photographic style and layout. We extract information about the image composition by storing the values of affective, aesthetic and artistic features in a compositional vector. We verify the discriminative power of our compositional vector for scene categorization by using it for the classification of images from various, diverse, large scale scene understanding datasets. We then combine the compositional features with traditional semantic features in a complete scene recognition framework. Results show that, due to the complementarity of compositional and semantic features, scene categorization systems indeed benefit from the incorporation of descriptors representing the image photographic layout (+ 13-15% over semantic-only categorization).