Natural scene classification using overcomplete ICA

Authors:
Jiebo Luo;Matthew Boutell
Affiliations:
Research and Development Laboratories, Eastman Kodak Company, Rochester, NY 14650, USA;Department of Computer Science, University of Rochester, USA
Venue:
Pattern Recognition
Year:
2005

Citing 8
Cited 6

Semantic based image retrieval: a probabilistic approach

MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope

International Journal of Computer Vision
Review of the State of the Art in Semantic Scene Classification

Review of the State of the Art in Semantic Scene Classification
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Content-Based Hierarchical Classification of Vacation Images

ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Bayesian fusion of camera metadata cues in semantic scene classification

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Man-made structure detection in natural images using a causal multiscale random field

CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition

Indoor versus outdoor scene classification using probabilistic neural network

EURASIP Journal on Applied Signal Processing
An exploratory study on joint analysis of visual classification in narrow domains and the discriminative power of tags

MS '08 Proceedings of the 2nd ACM workshop on Multimedia semantics
Image Classification Approach Based on Manifold Learning in Web Image Mining

ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Scene classification using pLSA with visterm spatial location

IMCE '09 Proceedings of the 1st international workshop on Interactive multimedia for consumer electronics
Scene classification using multiple features in a two-stage probabilistic classification framework

Neurocomputing
Building global image features for scene recognition

Pattern Recognition

Quantified Score

Hi-index	0.01

Visualization

Abstract

Principal component analysis (PCA) has been widely used to extract features for pattern recognition problems such as object recognition [Turk and Pentland, J. Cognitive Neurosci. 3(1) (1991)]. In natural scene classification, Oliva and Torralba presented such an algorithm in Oliva and Torralba [Int. J. Comput. Vision 42(3) (2001) 145-175] for representing images by their ''spatial envelope'' properties, including naturalness, openness, and roughness. Our implementation closely matched the original algorithm in accuracy for naturalness classification (or ''manmade-natural'' classification) on a similar (Corel) dataset [Dong and Luo, Towards holistic scene descriptors for semantic scene classification, Eastman Kodak Company Technical Report, October 1, 2003]. However, we found that consumer photos, which are far more unconstrained in content and imaging conditions, present a greater challenge for the algorithm (as they typically do for image understanding algorithms). In this paper, we present an alternative approach to more robust naturalness classification, using overcomplete independent components analysis (ICA) directly on the Fourier-transformed image to derive sparse representations as more effective features for classification. Using both heuristic and support vector machine classifiers, we demonstrated that our ICA-based features are superior to the PCA-based features used in Oliva and Torrabla [Int. J. Comput. Vision 42(3) (2001) 145-175]; Dong and Luo [Towards holistic scene descriptors for semantic scene classification, Eastman Kodak Company Technical Report, October 1, 2003]. In addition, we augment ICA-based features with camera metadata related to image capture conditions to further improve the performance of our algorithm.