Strongly convex optimization for joint fractal feature estimation and texture segmentation

Texture Segmentation via Haar Fractal Feature Estimation

Distributed Optimization for Non-Strongly Convex Regularizers

We develop primal-dual algorithms for distributed training of linear models in the Spark framework. We present the ProxCoCoA+ method which represents a generalization of the CoCoA+ algorithm and extends it to the case of general strongly convex regularizers. A primal-dual convergence rate analysis is provided along with an experimental evaluation of the algorithm on the problem of elastic net r...

Segmentation Using ‘new’ Texture Feature

Color, texture, shape and luminance are the prominent features for image segmentation. Texture is an organized group of spatial repetitive arrangements in an image and it is a vital attribute in many image processing and computer vision applications. The objective of this work is to segment the texture sub images from the given arbitrary image. The main contribution of this work is to introduce...

Image Segmentation using Texture Feature

This paper presents the image segmentation using the feature-based image segmentation tool. For segmentation, prior Watershed Transform tool was used. In this tool, to perform segmentation, additional techniques are required such as Distance Transform, Gradient. In FBIS technique engaging visual descriptors of images are known as Features. Any rule that captures the desired information from an ...

Extended fractal analysis for texture classification and segmentation

The Hurst parameter for two-dimensional (2-D) fractional Brownian motion (fBm) provides a single number that completely characterizes isotropic textured surfaces whose roughness is scale-invariant. Extended self-similar (ESS) processes were previously introduced in order to provide a generalization of fBm. These new processes are described by a number of multiscale Hurst parameters. In contrast...