Co-sparse textural similarity for interactive segmentation

Nieuwenhuis C, Hawe S, Kleinsteuber M, Cremers D (2014)

Publication Type: Conference contribution

Publication year: 2014

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Book Volume: 8694 LNCS

Pages Range: 285-301

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: CHE

ISBN: 9783319105987

DOI: 10.1007/978-3-319-10599-4_19

Abstract

We propose an algorithm for segmenting natural images based on texture and color information, which leverages the co-sparse analysis model for image segmentation. As a key ingredient of this method, we introduce a novel textural similarity measure, which builds upon the co-sparse representation of image patches. We propose a statistical MAP inference approach to merge textural similarity with information about color and location. Combined with recently developed convex multilabel optimization methods this leads to an efficient algorithm for interactive segmentation, which is easily parallelized on graphics hardware. The provided approach outperforms state-of-the-art interactive segmentation methods on the Graz Benchmark. © 2014 Springer International Publishing.

Involved external institutions

University of California, Berkeley US United States (USA) (US) Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Nieuwenhuis, C., Hawe, S., Kleinsteuber, M., & Cremers, D. (2014). Co-sparse textural similarity for interactive segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 285-301). CHE: Springer Verlag.

MLA:

Nieuwenhuis, Claudia, et al. "Co-sparse textural similarity for interactive segmentation." Proceedings of the 13th European Conference on Computer Vision, ECCV 2014, CHE Springer Verlag, 2014. 285-301.

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