Next Article in Journal
The Accuracy of 3D Optical Reconstruction and Additive Manufacturing Processes in Reproducing Detailed Subject-Specific Anatomy
Next Article in Special Issue
Partition and Inclusion Hierarchies of Images: A Comprehensive Survey
Previous Article in Journal
Color Texture Image Retrieval Based on Local Extrema Features and Riemannian Distance
Previous Article in Special Issue
Enhancing Face Identification Using Local Binary Patterns and K-Nearest Neighbors
Open AccessArticle

Baseline Fusion for Image and Pattern Recognition—What Not to Do (and How to Do Better)

School of Computer Science, University of St Andrews, St Andrews, Fife KY16 9SX, Scotland, UK
J. Imaging 2017, 3(4), 44;
Received: 18 July 2017 / Revised: 30 September 2017 / Accepted: 2 October 2017 / Published: 11 October 2017
(This article belongs to the Special Issue Computer Vision and Pattern Recognition)
The ever-increasing demand for a reliable inference capable of handling unpredictable challenges of practical application in the real world has made research on information fusion of major importance; indeed, this challenge is pervasive in a whole range of image understanding tasks. In the development of the most common type—score-level fusion algorithms—it is virtually universally desirable to have as a reference starting point a simple and universally sound baseline benchmark which newly developed approaches can be compared to. One of the most pervasively used methods is that of weighted linear fusion. It has cemented itself as the default off-the-shelf baseline owing to its simplicity of implementation, interpretability, and surprisingly competitive performance across a widest range of application domains and information source types. In this paper I argue that despite this track record, weighted linear fusion is not a good baseline on the grounds that there is an equally simple and interpretable alternative—namely quadratic mean-based fusion—which is theoretically more principled and which is more successful in practice. I argue the former from first principles and demonstrate the latter using a series of experiments on a diverse set of fusion problems: classification using synthetically generated data, computer vision-based object recognition, arrhythmia detection, and fatality prediction in motor vehicle accidents. On all of the aforementioned problems and in all instances, the proposed fusion approach exhibits superior performance over linear fusion, often increasing class separation by several orders of magnitude. View Full-Text
Keywords: prediction; arrhythmia; image matching; object recognition prediction; arrhythmia; image matching; object recognition
Show Figures

Figure 1

MDPI and ACS Style

Arandjelović, O. Baseline Fusion for Image and Pattern Recognition—What Not to Do (and How to Do Better). J. Imaging 2017, 3, 44.

AMA Style

Arandjelović O. Baseline Fusion for Image and Pattern Recognition—What Not to Do (and How to Do Better). Journal of Imaging. 2017; 3(4):44.

Chicago/Turabian Style

Arandjelović, Ognjen. 2017. "Baseline Fusion for Image and Pattern Recognition—What Not to Do (and How to Do Better)" J. Imaging 3, no. 4: 44.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop