DMCA
Object count/Area Graphs for the Evaluation of Object Detection and Segmentation Algorithms
| Venue: | International Journal on Document Analysis and Recognition |
| Citations: | 34 - 4 self |
BibTeX
@ARTICLE{Wolf_objectcount/area,
author = {Christian Wolf and Jean-michel Jolion and Bât Jules Verne},
title = {Object count/Area Graphs for the Evaluation of Object Detection and Segmentation Algorithms},
journal = {International Journal on Document Analysis and Recognition},
year = {},
pages = {2006}
}
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Abstract
Evaluation of object detection algorithms is a non-trivial task: a detection result is usually evaluated by comparing the bounding box of the detected object with the bounding box of the ground truth object. The commonly used precision and recall measures are computed from the overlap area of these two rectangles. However, these measures have several drawbacks: they don’t give intuitive information about the proportion of the correctly detected objects and the number of false alarms, and they cannot be accumulated across multiple images without creating ambiguity in their interpretation. Furthermore, quantitative and qualitative evaluation is often mixed resulting in ambiguous measures. In this paper we propose a new approach which tackles these problems. The performance of a detection algorithm is illustrated intuitively by performance graphs which present object level precision and recall depending on constraints on detection quality. In order to compare different detection algorithms, a representative single performance value is computed from the graphs. The influence of the test database on the detection performance is illustrated by performance/generality graphs. The evaluation method can be applied to different types of object detection algorithms. It has been tested on different text detection algorithms, among which are the participants of the ICDAR 2003 text detection competition.
Keyphrases
object count area graph object detection segmentation algorithm object detection algorithm detection result different detection algorithm performance generality graph new approach test database multiple image present object level precision overlap area detection quality false alarm non-trivial task performance graph detection performance ambiguous measure qualitative evaluation evaluation method recall measure several drawback text detection competition give intuitive information different type ground truth object detection algorithm detected object different text detection algorithm representative single performance value
