Name
Abstract | ||
|---|---|---|
Visual search data describe people's performance on the common perceptual problem of identifying target objects in a complex scene. Technological advances in areas such as eye tracking now provide researchers with a wealth of data not previously available. The goal of this work is to support researchers in analyzing this complex and multimodal data and in developing new insights into visual search techniques. We discuss several methods drawn from the statistics and machine learning literature for integrating visual search data derived from multiple sources and performing exploratory data analysis. We ground our discussion in a specific task performed by officers at the Transportation Security Administration and consider the applicability, likely issues, and possible adaptations of several candidate analysis methods. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-20816-9_52 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Visual search,Eye tracking,Data analysis,Transportation Security Administration | Transportation security administration,Data science,Visual search,Computer science,Human–computer interaction,Eye tracking,Exploratory data analysis,Perception | Conference |
Volume | ISSN | Citations |
9183 | 0302-9743 | 3 |
PageRank | References | Authors |
0.58 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David J. Stracuzzi | 1 | 91 | 25.68 |
| Ann Speed | 2 | 13 | 4.63 |
| Austin Silva | 3 | 21 | 4.86 |
| Michael Haass | 4 | 12 | 1.49 |
| Derek M. Trumbo | 5 | 8 | 1.90 |