Authors:
Marie Dumont
;
Raphaël Marée
;
Louis Wehenkel
and
Pierre Geurts
Affiliation:
University of Liège, Belgium
Keyword(s):
Image annotation, Machine learning, Decision trees, Extremely randomized trees, Structured outputs.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computer Vision, Visualization and Computer Graphics
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Image and Video Analysis
;
Segmentation and Grouping
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Statistical Approach
Abstract:
This paper addresses image annotation, i.e. labelling pixels of an image with a class among a finite set of predefined classes. We propose a new method which extracts a sample of subwindows from a set of annotated images in order to train a subwindow annotation model by using the extremely randomized trees ensemble method appropriately extended to handle high-dimensional output spaces. The annotation of a pixel of an unseen image is done by aggregating the annotations of its subwindows containing this pixel. The proposed method is compared to a more basic approach predicting the class of a pixel from a single window centered on that pixel and to other state-of-the-art image annotation methods. In terms of accuracy, the proposed method significantly outperforms the basic method and shows good performances with respect to the state-of-the-art, while being more generic, conceptually simpler, and of higher computational efficiency than these latter.