User-centric image segmentation using an interactive parameter adaptation tool

Pauplin, Olivier; Caleb-Solly, Praminda; Smith, Jim

Abstract

Creating successful machine vision systems often begins a process of developing customised reliable image segmentation algorithms for the detection, and possibly categorisation of regions of interest within images. This can require significant investment of time from both the image processing and the domain experts to set up. Frequently this process is mediated via interviews, or language-based systems which may not fully capture the visual decision-making process of the domain experts. The resulting algorithms can also often be "brittle" in the sense of being highly specialised to the task for which they are tuned, and are consequently sensitive to changes in operating conditions or image specifications. One approach is to use interactive evolution for developing rapidly reconfigurable systems in which the users' tacit knowledge and requirements can be elicited and used for finding the appropriate parameters to achieve the required segmentation without any need for specialised knowledge of the underlying machine vision systems. This paper presents an interactive tool that can be used to quickly and easily evolve optimal image segmentation parameters from scratch. Building on previous work, the new algorithm reported here incorporates user-guided local search and makes the fitness function more flexible to facilitate the underlying multi-objective decision-making process. One of the key requirements for any interactive system is a high level of usability, both in terms of effectiveness-being able to build accurate models that meet end-user requirements-and efficiency-being able to achieve the required results within a minimal amount of time and undue effort. The system described in this paper has been designed with these considerations in mind to ensure a high level of user-experience of the interaction process. We present results from a series of experiments with a range of users to analyse the effect of the improvements that have been made over the previous system. The efficiency of the tool is also tested with "novice users", and its usability by "novice users" is analysed. © 2009 Elsevier Ltd. All rights reserved.

• Journal Article Type: Article
• Publication Date: Feb 1, 2010
• Journal: Pattern Recognition
• Print ISSN: 0031-3203
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 43
• Issue: 2
• Pages: 519-529
• DOI: https://doi.org/10.1016/j.patcog.2009.03.007
• Keywords: interactive evolution, texture-based segmentation, evolutionary algorithm
• Public URL: https://uwe-repository.worktribe.com/output/981627
• Publisher URL: http://dx.doi.org/10.1016/j.patcog.2009.03.007