Content based Structural Recognition for Image Classification using PSO Technique and SVM

Abstract

The issue of SVMs parameter optimization with particle swarm optimization (pso) provide the optimum solution. This new classification approach may be an efficient alternative, in existing paradigms. PSO technique work with high dimensional datasets and mixed attribute data. The structure of the image is recognized through PSO technique which provide optimized parameter for SVM. This approach determines the performance of image classification after structural recognition based on content of image and comparing the obtained results with those reported for various other classification approaches. PSO-SVM technique can be applied mixed-attribute, hyperspectral data, hyperdimension spaces & problem description spaces and it can also be a competitive alternative to well established classification techniques. The optimized process of data reduces the unclassified region of support vector machine and improves the performance of image classification. The feature of region of image is classified by PSO-SVM technique in inside the image. Cassified features are increase recogniztion ratio because the feature of image is optimized. General TermsPattern Recognition, high dimensional image classification et. al.

A Fire Detection Algorithm Based on Tchebichef Moment Invariants and PSO-SVM

“…On the contrary, if they are disproportionately small, the detection accuracy rate will be too low to satisfy, making the model useless. PSO is a population-based global optimization algorithm from the simulation of birds feeding on behavior, proposed by Kennedy and Eberhart [39][40][41][42][43][44]. Owing to its advantageous characteristic of high efficiency, robustness, and easy implementation with code, the PSO algorithm is applied to optimize the parameters of SVM in this paper, combined with K-fold cross-validation.…”