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Techniques for Image Classification, Object Detection and Object Segmentation

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 5188)

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.

Supported by the Academy of Finland in the Finnish Centre of Excellence in Adaptive Informatics Research project.

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References

  1. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge (VOC2007) Results (2007), http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html

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  4. Viitaniemi, V., Laaksonen, J.: Techniques for image classification, object detection and object segmentation applied to VOC Challenge 2007. Technical Report 2, Department of Information and Computer Science, Helsinki University of Technology (TKK) (2008)

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© 2008 Springer-Verlag Berlin Heidelberg

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Viitaniemi, V., Laaksonen, J. (2008). Techniques for Image Classification, Object Detection and Object Segmentation. In: Sebillo, M., Vitiello, G., Schaefer, G. (eds) Visual Information Systems. Web-Based Visual Information Search and Management. VISUAL 2008. Lecture Notes in Computer Science, vol 5188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85891-1_26

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  • DOI: https://doi.org/10.1007/978-3-540-85891-1_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85890-4

  • Online ISBN: 978-3-540-85891-1

  • eBook Packages: Computer ScienceComputer Science (R0)