Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convex hulls of finite sets of points in two and three dimensions
Communications of the ACM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comprehensive Colour Image Normalization
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Learning a Sparse Representation for Object Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Image Indexing using Composite Color and Shape Invariant Features
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
The Amsterdam Library of Object Images
International Journal of Computer Vision
Component-Based Face Recognition with 3D Morphable Models
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Incorporating the Boltzmann Prior in Object Detection Using SVM
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Expert Systems with Applications: An International Journal
Coloring local feature extraction
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Combining color and shape information for illumination-viewpoint invariant object recognition
IEEE Transactions on Image Processing
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Object recognition is considered to be a predominant basic issue in computer vision. It is a challenging issue against inconsistent illumination, partial occlusion, changing background and shifting viewpoint, because considerable variations are exhibited by diversified real world patterns. The virtue of feature fusion lies in its reliability and capability for object recognition in terms of actual redundancy and complementary information. In this paper, we have developed an efficient hybrid approach using scale invariant features and machine learning techniques for object recognition. We extract the scale invariant features, namely color, shape and texture of the objects, separately with tile aid of suitable feature extraction techniques. Then, we integrate the color, shape and texture features of tile objects at the feature level, so as to improve the recognition performance. The fused feature set serves as a pattern for the forthcoming processes involved in the developed approach. Subsequently, we hybridize the process of object recognition by combining the pattern recognition algorithms like Support Vector Machine, Discriminant Canonical Correlation, and Locality Preserving Projections. Obviously, with three different pattern recognition algorithms employed, we are likely to get three distinct or identical results enumbered with false positives. So in order to reduce the number of false positives, we devise a decision module based on Neural Networks that takes in the match percentage from the chosen pattern recognition algorithms, and then decides the recognition result based on those match values. Our approach is evaluated on the Amsterdam Library of Object Images collection, a large collection of object images containing 1000 objects recorded under various imaging circumstances. The experimental results clearly demonstrate that our approach significantly outperforms the state-of-the-art methods for combining color, shape and texture features. The developed method is shown to be effective under a wide variety of imaging conditions. Finally, we employ empirical evaluation to evaluate our approach with the aid of an accuracy estimation method, such as k-fold cross validation.