Interleaved Feature Extraction Model: Bridging Multiple Techniques for Enhanced Object Identification

Abstract

Image mining, an essential process in many industrial image applications, has demonstrated significant utility in fields such as medical diagnostics, agriculture, industrial operations, space research, and education. This process involves extracting both information and image segments, but these tasks are often conducted independently, resulting in different workflows. This paper proposes an approach that integrates feature extraction and object recognition, leading to improved object identification. We introduce a novel method that improves recognition accuracy by increasing the percentage of optimal features. The ORB algorithm, known for its speed, is used in the initial pass, while the SURF algorithm is used as a secondary confirmation step for unrecognized objects. This approach supports the simultaneous processing of many images, which makes it suitable for large-scale applications such as image repositories in social media and expands the scope of research. This refined version maintains the core elements, while making the structure a little more fluid and coherent