Feature Selection for Image Retrieval based on Genetic Algorithm

Abstract

-This paper describes the development and implementation of feature selection for content based image retrieval. We are working on CBIR system with new efficient technique. In this system, we use multi feature extraction such as colour, texture and shape. The three techniques are used for feature extraction such as colour moment, gray level cooccurrence matrix and edge histogram descriptor. To reduce curse of dimensionality and find best optimal features from feature set using feature selection based on genetic algorithm. These features are divided into similar image classes using clustering for fast retrieval and improve the execution time. Clustering technique is done by k-means algorithm. The experimental result shows feature selection using GA reduces the time for retrieval and also increases the retrieval precision, thus it gives better and faster results as compared to normal image retrieval system. The result also shows precision and recall of proposed approach compared to previous approach for each image class. The CBIR system is more efficient and better performs using feature selection based on Genetic Algorithm.

Hyperparameter Optimization for Image Recognition over an AR-Sandbox Based on Convolutional Neural Networks Applying a Previous Phase of Segmentation by Color–Space

“…Furthermore, in Reference [20], the researchers proposed to reconstruct objects based on incomplete images and some information on the 3D object using active and passive methods to reconstruct high-resolution items. Additionally, mathematics is essential for image processing; in Reference [21], the researchers used an arithmetic method to find the right and more trustworthy solution for quantization tables, which are tables for image compression.…”

“…Finally, there is the image recognition component, where a CNN is implemented; due to the versatility that the CNN has with images, it is able to analyze them with distortions, such as different light conditions, different positions, and vertical and horizontal changes, among others. Additionally, with respect to other algorithms, the number of parameters was reduced, and therefore the training time was also reduced [20], which was the central theme of this research. It is important to highlight that the model will be obtained using the Random Search optimization algorithm.…”

Editor's Note

“…Next article, authored by Kushwaha and Welekar [3], describes a Content-Based Image Retrieval (CBIR) system that uses a genetic algorithm for feature selection. Features refer to color, texture, shape and other characteristics of the image, and automatically extracting these features allows searching relevant images from large databases based on features similarity.…”

Feature Selection using Genetic Algorithms

“…For feature selection using GA, the most natural and widely used chromosome encoding is the binary string encoding [17,18,19,20,21,22,23]. In this, the chromosome is represented as a bit string in which 1 represents if the feature is selected and 0 otherwise.…”

“…While the most common crossover and mutation operators are 2-point and random mutation respectively [21,24], some implementations make use of adaptive crossover and mutation [20,26], where the probability of crossover and mutation are learnt from iterations. While some implementations used variations of Elitist selection [19,20,24,27] or tournament [18], Roulette wheel seemed to be the most popular selection method [17,22,23]. To improve the results, local improvements were used in some cases where low performing features were replaced by high performing features [22].…”