Artistic ideation based on computer vision methods

• Autorzy: Reverter F. , Rosado P. , Figueras E. , Planas M.A. M.A.
• Języki publikacji: EN

Abstrakty

EN

This paper analyzes the automatic classification of scenes that are the basis of the ideation and the designing of the sculptural production of an artist. The main purpose is to evaluate the performance of the Bag-of-Features methods, in the challenging task of categorizing scenes when scenes differ in semantics rather than the objects they contain. We have employed a kernel-based recognition method that works by computing rough geometric correspondence on a global scale using the pyramid matching scheme introduced by Lazebnik [7]. Results are promising, on average the score is about 70%. Experiments suggest that the automatic categorization of images based on computer vision methods can provide objective principles in cataloging images.

Słowa kluczowe

EN

bag-of-features; SIFT descriptors; pyramid match kernel; artistic ideation

• Wydawca: Komisja Informatyki Polskiej Akademii Nauk, Oddział w Gdańsku
• Czasopismo: Journal of Theoretical and Applied Computer Science
• Rocznik: 2012
• Tom: Vol. 6, nr 2
• Strony: 72--78
• Opis fizyczny: Bibliogr. 11 poz., rys., tab.

Twórcy

autor

Reverter F.

autor

Rosado P.

autor

Figueras E.

autor

Planas M.A. M.A.

• University of Barcelona, Spain,

Bibliografia

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• [2] Lowe, D. G. 2004 Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2),91:110.
• [3] Fei-Fei, L and Perona, P. 2005. A Bayesian hierarchical model for learning natural scene categories. In Proceedings of CVPR.
• [4] Sivic, J., Russell, B. C., Efros, A. A., Zisserman, A., and Freeman, W. T. 2005. Discovering objects and their location in image collections. In Proceedings of IEEE International Conference on Computer Vision, Beijing.
• [5] Quelhas, P., Monay, F., Odobez, J.-M., Gatica-Perez, D., Tuytelaars, T., and Gool, L. V. 2005. Modeling scenes with local descriptors and latent aspects. In Proceedings of IEEE International Conference on Computer Vision (ICCV), Beijing.
• [6] Bosch, A., Zisserman, A., Mu~noz, X. 2007. Image Classification using Random Forests and Ferns. In Proceedings of IEEE International Conference on Computer Vision (ICCV).
• [7] Lazebnik, S., Schmid, C., and Ponce, J. 2006. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proceedings of CVPR.
• [8] Grauman, K. and Darrel, T. 2005. The pyramid match kernel: Discriminative classification with sets of image features. In Proceedings of IEEE International Conference on Computer Vision (ICCV), Beijing.
• [9] Zhang,J., Marszaek,M., Lazebnik, C., and Schmid, S. 2007. Local features and kernels for classification of texture and object categories: a comprehensive study. International Journal of Computer Vision. DOI: 10.1007/s11263-006-9794-4.
• [10] Vedaldi, A.,and Zisserman, A. 2010. Efficient Additive Kernels via Explicit Feature Maps. In Proceedings of CVPR.
• [11] Vedaldi, A., and Fulkerson, B. 2008. VLFeat library (http://www.vlfeat.org/).