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Title: User-Driven Sampling Strategies in Image Exploitation

Journal Article · · Proceedings of SPIE - The International Society for Optical Engineering
DOI:https://doi.org/10.1117/12.2038581· OSTI ID:1234822
 [1];  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
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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, Vol. 9017; ISSN 0277-786X
Publisher:
SPIECopyright Statement
Country of Publication:
United States
Language:
English

References (6)

Interactive Machine Learning in Data Exploitation journal September 2013
SIGKDD Special Interest Group on Knowledge Discovery in Data journal December 2005
Semantic Interaction for Sensemaking: Inferring Analytical Reasoning for Model Steering journal December 2012
Relevance feedback: a power tool for interactive content-based image retrieval journal January 1998
Proceedings of the 25th international conference on Machine learning - ICML '08 conference July 2008
Proceedings of the 8th international conference on Intelligent user interfaces conference January 2003

Cited By (3)

A Review of User Interface Design for Interactive Machine Learning text January 2018
Power to the Oracle? Design Principles for Interactive Labeling Systems in Machine Learning journal January 2020
A Review of User Interface Design for Interactive Machine Learning journal July 2018

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Related Subjects

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