A Method to Recognize and Count Leaves on the Surface of a River Using User’s Knowledge about Color of Leaves

Tsutsumi, Fujio; Tateda, Yutaka

doi:10.1007/978-3-642-00399-8_18

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Crayons [Fails and Olsen 2003] is the often cited formative paper in IML that demonstrates an interface whereby users can incrementally train a classifier by directly drawing on an image. This approach has also been leveraged by Tsutsumi and Tateda [2009] to help measure the amount of leaf debris flowing on a river as well as for analysis of satellite, medical and material science images ([Porter et al 2011], [Porter et al 2013], [Harvey and Porter 2016], [Kreshuk et al 2011]).…”

Section: Imagesmentioning

confidence: 99%

A Review of User Interface Design for Interactive Machine Learning

Dudley

Kristensson

2018

ACM Trans. Interact. Intell. Syst.

224

137

View full text Add to dashboard Cite

Interactive Machine Learning (IML) seeks to complement human perception and intelligence by tightly integrating these strengths with the computational power and speed of computers. The interactive process is designed to involve input from the user but does not require the background knowledge or experience that might be necessary to work with more traditional machine learning techniques. Under the IML process, non-experts can apply their domain knowledge and insight over otherwise unwieldy datasets to find patterns of interest or develop complex data-driven applications. This process is co-adaptive in nature and relies on careful management of the interaction between human and machine. User interface design is fundamental to the success of this approach, yet there is a lack of consolidated principles on how such an interface should be implemented. This article presents a detailed review and characterisation of Interactive Machine Learning from an interactive systems perspective. We propose and describe a structural and behavioural model of a generalised IML system and identify solution principles for building effective interfaces for IML. Where possible, these emergent solution principles are contextualised by reference to the broader human-computer interaction literature. Finally, we identify strands of user interface research key to unlocking more efficient and productive non-expert interactive machine learning applications.

show abstract