User-Driven Sampling Strategies in Image Exploitation

Journal Article · Mon Dec 23 00:00:00 EST 2013 · Proceedings of SPIE - The International Society for Optical Engineering

DOI:https://doi.org/10.1117/12.2038581· OSTI ID:1234822

Harvey, Neal R. ^[1]; Porter, Reid B. ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the human-computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data is to be labeled at each iteration. User-driven sampling strategies can emerge in many visual analytics applications but they have not been fully developed in machine learning. We discovered that in user-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this paper we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools towards local minima that have lower error than tools trained with all of the data. Furthermore, in preliminary experiments we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC52-06NA25396
• OSTI ID: 1234822
• Report Number(s): LA-UR--14-28017
• Journal Information: Proceedings of SPIE - The International Society for Optical Engineering, Journal Name: Proceedings of SPIE - The International Society for Optical Engineering Vol. 9017; ISSN 0277-786X
• Publisher: SPIE
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
computing systems
feedback
human-computer interaction
image analysis
machine learning
visual analytics
visualization