Multiple Kernel Learning for Object Classification
نویسندگان
چکیده
Combining information from various image descriptors has become a standard technique for image classification tasks. Multiple kernel learning (MKL) approaches allow to determine the optimal combination of such similarity matrices and the optimal classifier simultaneously. Most MKL approaches employ an `-regularization on the mixing coefficients to promote sparse solutions; an assumption that is often violated in image applications where descriptors hardly encode orthogonal pieces of information. In this paper, we compare `-MKL with a recently developed non-sparse MKL in object classification tasks. We show that the non-sparse MKL outperforms both the standard MKL and SVMs with average kernel mixtures on the PASCAL VOC data sets.
منابع مشابه
Neural Network-Based Learning Kernel for Automatic Segmentation of Multiple Sclerosis Lesions on Magnetic Resonance Images
Background: Multiple Sclerosis (MS) is a degenerative disease of central nervous system. MS patients have some dead tissues in their brains called MS lesions. MRI is an imaging technique sensitive to soft tissues such as brain that shows MS lesions as hyper-intense or hypo-intense signals. Since manual segmentation of these lesions is a laborious and time consuming task, automatic segmentation ...
متن کاملMulti-label Multiple Kernel Learning by Stochastic Approximation: Application to Visual Object Recognition
Recent studies have shown that multiple kernel learning is very effective for object recognition, leading to the popularity of kernel learning in computer vision problems. In this work, we develop an efficient algorithm for multi-label multiple kernel learning (ML-MKL). We assume that all the classes under consideration share the same combination of kernel functions, and the objective is to fin...
متن کاملOnline Learning with (Multiple) Kernels: A Review
This review examines kernel methods for online learning, in particular, multiclass classification. We examine margin-based approaches, stemming from Rosenblatt's original perceptron algorithm, as well as nonparametric probabilistic approaches that are based on the popular gaussian process framework. We also examine approaches to online learning that use combinations of kernels--online multiple ...
متن کاملMulti-task Learning via Non-sparse Multiple Kernel Learning
In object classification tasks from digital photographs, multiple categories are considered for annotation. Some of these visual concepts may have semantic relations and can appear simultaneously in images. Although taxonomical relations and co-occurrence structures between object categories have been studied, it is not easy to use such information to enhance performance of object classificatio...
متن کاملSparse and Non-sparse Multiple Kernel Learning for Recognition
The development of Multiple Kernel Techniques has become of particular interest for machine learning researchers in Computer Vision topics like image processing, object classification, and object state recognition. Sparsity-inducing norms along with non-sparse formulations promote different degrees of sparsity at the kernel coefficient level, at the same time permitting non-sparse combination w...
متن کامل