Machine assisted histogram classification

Color Histogram Classification using NMF

Histogram intersection kernel for image classification

In this paper we address the problem of classifying images, by exploiting global features that describe color and illumination properties, and by using the statistical learning paradigm. The contribution of this paper is twofold. First, we show that histogram intersection has the required mathematical properties to be used as a kernel function for Support Vector Machines (SVMs). Second, we give...

SVMs for Histogram-Based Image Classification

Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that Support Vector Machines (SVM) can generalize well on difficult image classification problems where the only features are high dimensional histograms. Heavy-tailed RBF kernels of the form K(x,y) = e−ρ P i |x i −y i | with a ≤ 1 and ...

Histogram Binning and Morphology based Image Classification

A hyperspectral image is characterized by a large dimensionality data, recorded at very fine spectral resolution in hundreds of narrow frequency bands. These bands provide a wealth of spatial and spectral information of the scene, imaged by the sensors. Histogram binning and morphological operator based Extreme Learning Machine classifier is proposed. Histogram binning and morphological operato...

Local Quantization Code histogram for texture classification

In this paper, an efficient local operator, namely the Local Quantization Code (LQC), is proposed for texture classification. The conventional local binary pattern can be regarded as a special local quantization method with two levels, 0 and 1. Some variants of the LBP demonstrate that increasing the local quantization level can enhance the local discriminative capability. Hence, we present a s...