Thesis: Multiple Kernel Learning for Object Categorization

TL;DR

This paper introduces novel multiple kernel learning methods with l-infinity and mixed norm regularizations for object categorization, leading to improved recognition accuracy over traditional single-kernel approaches.

Contribution

It develops new MKL formulations based on block l-infinity and mixed norms, optimizing kernel combination rather than selection, and demonstrates their effectiveness on benchmark datasets.

Findings

Significant accuracy improvements on benchmark datasets.

New MKL formulations outperform existing block l-1 norm methods.

Efficient algorithms for the proposed formulations are provided.

Abstract

Object Categorization is a challenging problem, especially when the images have clutter background, occlusions or different lighting conditions. In the past, many descriptors have been proposed which aid object categorization even in such adverse conditions. Each descriptor has its own merits and de-merits. Some descriptors are invariant to transformations while the others are more discriminative. Past research has shown that, employing multiple descriptors rather than any single descriptor leads to better recognition. The problem of learning the optimal combination of the available descriptors for a particular classification task is studied. Multiple Kernel Learning (MKL) framework has been developed for learning an optimal combination of descriptors for object categorization. Existing MKL formulations often employ block l-1 norm regularization which is equivalent to selecting a single kernel from a library of kernels. Since essentially a single descriptor is selected, the existing formulations maybe sub- optimal for object categorization. A MKL formulation based on block l-infinity norm regularization has been developed, which chooses an optimal combination of kernels as opposed to selecting a single kernel. A Composite Multiple Kernel Learning(CKL) formulation based on mixed l-infinity and l-1 norm regularization has been developed. These formulations end in Second Order Cone Programs(SOCP). Other efficient alter- native algorithms for these formulation have been implemented. Empirical results on benchmark datasets show significant improvement using these new MKL formulations.