Authors:
Johannes Steffen
;
Marko Rak
;
Tim König
and
Klaus-Dietz Tönnies
Affiliation:
Otto von Guericke University Magdeburg, Germany
Keyword(s):
Object Detection, Object Segmentation, Cosegmentation.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Clustering
;
Computer Vision, Visualization and Computer Graphics
;
Image Understanding
;
Information Retrieval and Learning
;
Instance-Based Learning
;
Object Recognition
;
Pattern Recognition
;
Software Engineering
;
Theory and Methods
Abstract:
We tackle the problem of unsupervised object cosegmentation combining automatic image selection, cosegmentation, and knowledge transfer to yet unlabelled images. Furthermore, we overcome the limitations often present in state-of-the-art methods in object cosegmentation, namely, high complexity and poor scalability w.r.t. image set size. Our proposed approach is robust, reasonably fast, and scales linearly w.r.t. the image set size. We tested our approach on two commonly used cosegmentation data sets and outperformed some of the state-of-the-art methods using significantly less information than possible. Additionally, results indicate the applicability of our approach on larger image sets.