An Integrated Framework for Information Identification With Image Data Using Multi-Technique Feature Extraction

Introduction

Information identification has been guided by a fresh perspective with the intensification of digital era. The popularity and growth of image capturing devices have prioritized images as a familiar media of communication for the mass (Korytkowski, Rutkowski, & Scherer, 2016). The image datasets have been archived and maintained as rich sources of valuable information (Ahmadian, & Mostafa, 2003). Traditional means of information identification with images have been based on text-based recognition system. The process used to map images with text-based annotations. However, text or keywords based mapping of images has insufficient information about image contents and is based on the perception of the individual performing the job of annotation (Walia, Goyal, & Brar, 2014). These are major drawbacks for recognition of information with images and to escalate the success rate.

The discovery of a object of interest or locating the region of interest in a picture or an arrangement of pictures, which has applications in confront acknowledgment and in addition to video conferencing frameworks, is a testing assignment and has been considered by numerous researchers (Das, & Walia, 2017). Once the test picture is removed from the scene, its gray level and size are standardized before putting away or testing. In a few applications, for example, distinguishing proof of international ID pictures or drivers' licenses, conditions of picture obtaining are typically so controlled that a portion of the pre-processing stages may not be essential.