Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
A Statistical Approach to Texture Classification from Single Images
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
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
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
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 Visual Vocabulary for Flower Classification
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Dissimilarity-Based Detection of Schizophrenia
WBD '10 Proceedings of the 2010 First Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging
Computational TMA analysis and cell nucleus classification of renal cell carcinoma
Proceedings of the 32nd DAGM conference on Pattern recognition
A framework for selection and fusion of pattern classifiers in multimedia recognition
Pattern Recognition Letters
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Feature combination is used in object classification to combine the strength of multiple complementary features and yield a more powerful feature. While some work can be found in literature to calculate the weights of features, the selection of features used in combination is rarely touched. Different researchers usually use different sets of features in combination and obtain different results. It's not clear to which degree the superior combination results should be attributed to the combination methods and not the carefully selected feature sets. In this paper we evaluate the impact of various feature-related factors on feature combination performance. Specifically, we studied the combination of various popular descriptors, kernels and spatial pyramid levels through extensive experiments on four datasets of diverse object types. As a result, we provide some empirical guidelines on designing experimental setups and combination algorithms in object classification.