Psychology Analysis using Color Histogram Clustering

K-Means Clustering Using Localized Histogram Analysis

The first step required for many applications and algorithms is the segmentation or clustering of an image. This intuitively easy problem is actually very difficult for a computer to automatically perform. While having an effective solution to this problem would be valuable for all types of image processing, it would be especially useful in medical imaging. One method that addresses this proble...

Color Histogram Classification using NMF

Local vs. Global Histogram-Based Color Image Clustering

In this paper, we present two image clustering techniques to automatically group color images that correlate with semantic concepts. This work goes towards satisfying the ever growing need for techniques that are capable of automatically generating semantic concepts for images from their visual features. We present two techniques and evaluate their relative performances based on the perceptual ...

Multiresolution Histogram Analysis for Color Reduction

A new technique for color reduction is presented, based on the analysis of the histograms of an image at different resolutions. Given an input image, lower resolution images are generated by using a scaling down interpolation method. Then, peaks and pits that are present in the histograms at all resolutions and dominate in the histogram of the input image at full resolution are taken into accou...

Image Indexing Using Weighted Color Histogram

Image indexing is the process of image retrieval from databases of images or videos based on their contents. Specifically, histogram-based algorithms are considered to be effective for color image indexing. We suggest a new method of color space quantization in the CIELUV color space, named weighted LUV quantization. With this method, each bin in the LUV space has a different weighting factor, ...