Computer Vision for Supporting Image Search

TL;DR

This paper reviews recent advances in computer vision, emphasizing the need for improved image search capabilities that support human memory limitations, and discusses the requirements for systems to facilitate effective image retrieval.

Contribution

It highlights the gap in current image search technologies and outlines the necessary computer vision requirements to develop better image retrieval systems based on human memory insights.

Findings

Computer vision has achieved human-level accuracy in many tasks.

Current image search methods are insufficient for user needs.

Understanding human memory can guide better image retrieval system design.

Abstract

Computer vision and multimedia information processing have made extreme progress within the last decade and many tasks can be done with a level of accuracy as if done by humans, or better. This is because we leverage the benefits of huge amounts of data available for training, we have enormous computer processing available and we have seen the evolution of machine learning as a suite of techniques to process data and deliver accurate vision-based systems. What kind of applications do we use this processing for ? We use this in autonomous vehicle navigation or in security applications, searching CCTV for example, and in medical image analysis for healthcare diagnostics. One application which is not widespread is image or video search directly by users. In this paper we present the need for such image finding or re-finding by examining human memory and when it fails, thus motivating the need for a different approach to image search which is outlined, along with the requirements of computer vision to support it.