HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

Accurate Object Recognition with Shape Masks

Marcin Marszałek 1 Cordelia Schmid 1
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : In this paper we propose an object recognition approach that is based on shape masks--generalizations of segmentation masks. As shape masks carry information about the extent (outline) of objects, they provide a convenient tool to exploit the geometry of objects. We apply our ideas to two common object class recognition tasks--classification and localization. For classification, we extend the orderless bag-of-features image representation. In the proposed setup shape masks can be seen as weak geometrical constraints over bag-of-features. Those constraints can be used to reduce background clutter and help recognition. For localization, we propose a new recognition scheme based on high-dimensional hypothesis clustering. Shape masks allow to go beyond bounding boxes and determine the outline (approximate segmentation) of the object during localization. Furthermore, the method easily learns and detects possible object viewpoints and articulations, which are often well characterized by the object outline. Our experiments reveal that shape masks can improve recognition accuracy of state-of-the-art methods while returning richer recognition answers at the same time. We evaluate the proposed approach on the challenging natural-scene Graz-02 object classes dataset.
Document type :
Journal articles
Complete list of metadata

Contributor : Thoth Team Connect in order to contact the contributor
Submitted on : Monday, December 12, 2011 - 3:04:07 PM
Last modification on : Thursday, January 20, 2022 - 5:28:03 PM

Links full text




Marcin Marszałek, Cordelia Schmid. Accurate Object Recognition with Shape Masks. International Journal of Computer Vision, Springer Verlag, 2012, 97 (2), pp.191-209. ⟨10.1007/s11263-011-0479-2⟩. ⟨hal-00650941⟩



Record views