Techniques for Image Classification, Object Detection and Object Segmentation

Authors:
Ville Viitaniemi;Jorma Laaksonen
Affiliations:
Department of Information and Computer Science, Helsinki University of Technology, TKK, Finland FI-02015;Department of Information and Computer Science, Helsinki University of Technology, TKK, Finland FI-02015
Venue:
VISUAL '08 Proceedings of the 10th international conference on Visual Information Systems: Web-Based Visual Information Search and Management
Year:
2008

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Abstract

In this paper we outline the techniques which we used to participate in the PASCAL NoE VOC Challenge 2007 image analysis performance evaluation campaign. We took part in three of the image analysis competitions: image classification, object detection and object segmentation. In the classification task of the evaluation our method produced comparatively good performance, the 4th best of 19 submissions. In contrast, our detection results were quite modest. Our method's segmentation accuracy was the best of all submissions. Our approach for the classification task is based on fused classifications by numerous global image features, including histograms of local features. The object detection combines similar classification of automatically extracted image segments and the previously obtained scene type classifications. The object segmentations are obtained in a straightforward fashion from the detection results.