Object Recognition to Content Based Image Retrieval: A Study of the Developments and Applications of Computer Vision

Abstract

Natural Language Processing (NLP) and Computer Vision (CV) are interconnected fields within the domain of Artificial Intelligence (AI). CV is tasked with the process of engaging with computer systems to effectively interpret and recognize visual data, while NLP is responsible for comprehending and processing the human voice. The two fields have practical applicability in various tasks such as image description generation, object recognition, and question-based answering after a visual input. Deep learning algorithms such as word input are typically employed in enhancing the performance of Content-Based Image Processing (CBIR) techniques. Generally, NLP and CV play a vital role in enhancing computer comprehension and engagements with both visual and written information. This paper seeks to review various major elements of computer vision, such as CBIR, visual effects, image documentation, video documentation, visual learning, and inquiry to explore various databases, techniques, and methods employed in this field. The authors focus on the challenges and progress in each area and offer new strategies for improving the performance of CV systems.