User-centric image segmentation using an interactive parameter adaptation tool

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.

[1]  Keith L. Downing,et al.  Introduction to Evolutionary Algorithms , 2006 .

[2]  Yvonne Rogers,et al.  Interaction Design: Beyond Human-Computer Interaction. Second Edition , 2007 .

[3]  Praminda Caleb-Solly,et al.  Interactive Evolutionary Strategy Based Discovery of Image Segmentation Parameters , 2004 .

[4]  Aleksandra Mojsilovic,et al.  Adaptive perceptual color-texture image segmentation , 2005, IEEE Transactions on Image Processing.

[5]  R. Dawkins The Blind Watchmaker , 1986 .

[6]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[7]  Miho Ohsaki,et al.  Interactive Evolutionary Computation-Based Hearing Aid Fitting , 2007, IEEE Transactions on Evolutionary Computation.

[8]  Ashutosh Tiwari,et al.  An interactive genetic algorithm-based framework for handling qualitative criteria in design optimization , 2007, Comput. Ind..

[9]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[10]  Hideyuki Takagi,et al.  Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation , 2001, Proc. IEEE.

[11]  Kevin Kok Wai Wong,et al.  Classification of adaptive memetic algorithms: a comparative study , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  Xinjie Yu,et al.  Introduction to evolutionary algorithms , 2010, The 40th International Conference on Computers & Indutrial Engineering.

[13]  Nikolaus Hansen,et al.  The CMA Evolution Strategy: A Comparing Review , 2006, Towards a New Evolutionary Computation.

[14]  Praminda Caleb-Solly,et al.  Adaptive surface inspection via interactive evolution , 2007, Image Vis. Comput..

[15]  Lauren O'Donnell,et al.  Semi-automatic medical image segmentation , 2001 .

[16]  James Smith,et al.  A tutorial for competent memetic algorithms: model, taxonomy, and design issues , 2005, IEEE Transactions on Evolutionary Computation.

[17]  Pierrick Legrand,et al.  Interactive evolution for cochlear implants fitting , 2007, Genetic Programming and Evolvable Machines.

[18]  Yvonne Rogers,et al.  Interaction Design: Beyond Human-Computer Interaction , 2002 .

[19]  Sabine Moisan,et al.  Experience in Integrating Image Processing Programs , 1999, ICVS.

[20]  Euripides G. M. Petrakis,et al.  A survey on industrial vision systems, applications, tools , 2003, Image Vis. Comput..

[21]  Benoît Georis Program supervision techniques for easy configuration of video understanding systems , 2006 .

[22]  Vincent Martin,et al.  A Learning Approach for Adaptive Image Segmentation , 2006, Fourth IEEE International Conference on Computer Vision Systems (ICVS'06).

[23]  Praminda Caleb-Solly,et al.  Incorporation of adaptive mutation based on subjective evaluation in an interactive evolution strategy , 2005, 2005 IEEE Congress on Evolutionary Computation.

[24]  Edmund K. Burke,et al.  An evolutionary approach to cancer chemotherapy scheduling , 2007, Genetic Programming and Evolvable Machines.

[25]  Jim E. Smith,et al.  Coevolving Memetic Algorithms: A Review and Progress Report , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[26]  Pascal Fua,et al.  Using Generic Geometric Models for Intelligent Shape Extraction , 1987, AAAI.

[27]  Damjan Zazula,et al.  Assessing the Efficiency of the Image Segmentation Algorithms , 2001 .

[28]  Kee Tung. Wong,et al.  Texture features for image classification and retrieval. , 2002 .