Object count/area graphs for the evaluation of object detection and segmentation algorithms
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Evaluation of object detection algorithms is a non-trivial task: a detection result is usually evaluated by comparing the bounding box of the detected object with the bounding box of the ground truth object. The commonly used precision and recall measures are computed from the overlap area of these two rectangles. However, these measures have several drawbacks: they don't give intuitive information about the proportion of the correctly detected objects and the number of false alarms, and they cannot be accumulated across multiple images without creating ambiguity in their interpretation. Furthermore, quantitative and qualitative evaluation is often mixed resulting in ambiguous measures.
In this paper we propose a new approach which tackles these problems. The performance of a detection algorithm is illustrated intuitively by performance graphs which present object level precision and recall depending on constraints on detection quality. In order to compare different detection algorithms, a representative single performance value is computed from the graphs. The influence of the test database on the detection performance is illustrated by performance/generality graphs. The evaluation method can be applied to different types of object detection algorithms. It has been tested on different text detection algorithms, among which are the participants of the ICDAR 2003 text detection competition.
KeywordsEvaluation Object detection Text detection
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- 1.Aloimonos, Y., Rosenfeld, A.: REPLY: A response to “ignorance, Myopia and Naiveté in computer vision systems” by R.C. Jain and T.O. Binford. CVGIP: Image Understanding 53(1), 120–124 (1991)Google Scholar
- 2.Antonacopoulos, A., Brough, A.: Methodology for flexible and efficient analysis of the performance of page segmentation algorithms. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 451–454 (1999)Google Scholar
- 3.Antonacopoulos, A., Gatos, B., Karatzas, D.: ICDAR 2003 Page Segmentation Competition. In Proceedings of the International Conference on Document Analysis and Recognition, pp. 688–692 (2003)Google Scholar
- 5.Doermann, D., Mihalcik, D.: Tools and techniques for video performance evaluation. In: Proceedings of the International Conference on Pattern Recognition, vol. 4, pp. 4167–4170 (2000)Google Scholar
- 9.Huijsmans, N., Sebe, N.: Extended performance graphs for cluster retrieval. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 26–31 (2001)Google Scholar
- 11.Landais, R., Vinet, L., Jolion, J.-M.: A goal directed methodology for groundtruthing and evaluating a commercial OCR. Pattern Recognition (submitted) (2004)Google Scholar
- 12.Liang, J., Phillips, I.T., Haralick, R.M.: Performance evaluation of document layout analysis algorithms on the UW data set. In Document Recognition IV, Proceedings of the SPIE, pp. 149–160 (1997)Google Scholar
- 13.Lucas, S.M., Panaretos, A., Sosa, L., Tang, A., Wong, S., Young, R.: ICDAR 2003 robust reading competitions. In: Proceedings of the Seventh International Conference on Document Analysis and Recognition, vol. 2, pp. 682–687 (2003)Google Scholar
- 14.Lucas, S.M., Panaretos, A., Sosa, L., Tang, A., Wong, S., Young, R., Ashida, K., Nagai, H., Okamoto, M., Yamamoto, H., Miyao, H., Zhu, J., Ou, W., Wolf, C., Jolion, J.-M., Todoran, L., Worring, M., Lin, X.: ICDAR 2003 robust reading competitions: entries, results and future directions. International Journal on Document Analysis and Recognition - Special Issue on Camera-based Text and Document Recognition 7(2–3), 105–122 (2005)Google Scholar
- 15.Mariano, V.Y., Min, J., Park, J.-H., Kasturi, R., Mihalcik, D., Li, H., Doermann, D., Drayer, T.: Performance evaluation of object detection algorithms. In: Proceedings of the International Conference on Pattern Recognition, vol. 3, pp. 965–969 (2002)Google Scholar
- 18.Taylor, G.W., Wolf, C.: Reinforcement learning for parameter control of text detection in images and video sequences. In: Proceedings of the International Conference on Information & Communication Technologies (IEEE), 2004. IEEE Section France (2004)Google Scholar
- 19.van Rijsbergen, C.J.: Information retrieval, 2nd edition. Butterworths, London (1979)Google Scholar
- 21.Wenyin, L., Dori, D.: A protocol for performance evalution of line detection algorithms. Machine Vision and Applications: Special Issue on Performance Evaluation 9(5–6), 240–250 (1997)Google Scholar
- 22.Wolf, C.: Text Detection in Images taken from Videos Sequences for Semantic Indexing. PhD thesis, INSA de Lyon, 20, rue Albert Einstein, 69621 Villeurbanne Cedex, France (2003)Google Scholar