Learning Kernel for Conditional Moment-Matching Discrepancy-Based Image Classification

Local Coding Based Matching Kernel Method for Image Classification

This paper mainly focuses on how to effectively and efficiently measure visual similarity for local feature based representation. Among existing methods, metrics based on Bag of Visual Word (BoV) techniques are efficient and conceptually simple, at the expense of effectiveness. By contrast, kernel based metrics are more effective, but at the cost of greater computational complexity and increase...

Multiple Similarities Based Kernel Subspace Learning for Image Classification

In this paper, we propose a new method for image classification, in which matrix based kernel features are designed to capture the multiple similarities between images in different low-level visual cues. Based on the property that dot product kernel can be regarded as a similarity measure, we apply kernel functions to different low-level visual features respectively to measure the similarities ...

Kernel based Extreme Learning Machines for Image Classification

This paper investigates possibilities for application of Kernel based Extreme Learning Machines (K-ELM) to the problem of multiclass image classification. It is combined with Local Binary Pattern (LBP) image descriptor, to reach highly accurate results. LBP is widely used global image descriptor characterized by compactness and robustness to illumination and resolution changes. Classification i...

Sparse Kernel Learning for Image Set Classification

No single universal image set representation can efficiently encode all types of image set variations. In the absence of expensive validation data, automatically ranking representations with respect to performance is a challenging task. We propose a sparse kernel learning algorithm for automatic selection and integration of the most discriminative subset of kernels derived from different image ...

Kernel learning approaches for image classification