Finding Optimal Combination of Kernels using Genetic Programming
This work addresses the need for better feature combination in object categorization, particularly in challenging conditions, but is incremental as it builds on existing Multiple Kernel Learning methods.
The paper tackles the problem of object categorization in computer vision by proposing a method to find optimal non-linear combinations of kernels using genetic programming, achieving improved accuracy compared to linear combinations.
In Computer Vision, problem of identifying or classifying the objects present in an image is called Object Categorization. It is a challenging problem, especially when the images have clutter background, occlusions or different lighting conditions. Many vision features have been proposed which aid object categorization even in such adverse conditions. Past research has shown that, employing multiple features rather than any single features leads to better recognition. Multiple Kernel Learning (MKL) framework has been developed for learning an optimal combination of features for object categorization. Existing MKL methods use linear combination of base kernels which may not be optimal for object categorization. Real-world object categorization may need to consider complex combination of kernels(non-linear) and not only linear combination. Evolving non-linear functions of base kernels using Genetic Programming is proposed in this report. Experiment results show that non-kernel generated using genetic programming gives good accuracy as compared to linear combination of kernels.