Abstract
The taxonomical recognition of microscopic biological particles such as pollen and spores is relevant for medical and aerobiological applications. Focusing on an accurate and automatic vision-based pollen recognition system, we propose a method for classification of pollen apertures based on bag-of-words strategy, with the ability of learning new types from different taxa without the need of new algorithms. Results demonstrate suitable performance and ability to add new taxa.
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Erdtman, G.: An Introduction To Pollen Analysis. Chronica Botanica Company, USA (1943)
Hesse, M., Halbritter, H., Weber, M., Buchner, R., Frosch- Radivo, A., Ulrich, S.: Pollen Terminology. In: An Illustrated Handbook, Springer, Austria (2009)
Boucher, A., Hidalgo, P.J., Thonnat, M., Belmonte, J., Galan, C., Bonton, P., Tomczak, R.: Development of a semi-automatic system for pollen recognition. Aerobiologia 18(3), 195–201 (2002)
Chen, C., Hendriks, E.A., Duin, R.P., Reiber, J., Hiemstra, P., De Weger, L., Stoel, B.: Feasibility study on automated recognition of allergenic pollen: grass, birch and mugwort. Aerobiologia 22, 275–284 (2006)
Csurka, G., Dance, C., Bray, C., Fan, L., Willamowski, J.: Visual categorization with bags of keypoints. In: Pattern Recognition and Machine Learning in Computer Vision Workshop, ECCV Grenoble, France, pp. 1–22 (2004)
Wu, J., Tan, W.-C., Rehg, J.M.: Efficient and Effective Visual Codebook Generation Using Additive Kernels. Journal of Machine Learning Research 12, 3097–3118 (2011), Georgia Institute of Technology
López-Sastre, R.J., Tuytelaars, T., Acevedo-Rodríguez, F.J., Maldonado-Bascón, S.: Towards a more discriminative and semantic visual vocabulary. Computer Vision and Image Understanding 115, 415–425 (2011)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1615–1630 (2005)
Schmid, C., Mohr, R.: Local grayvalue invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(5), 530–535 (1997)
Koenderink, J.J., Doorn, A.J.: Representation of local geometry in the visual system. Biological Cybernetics 5(6), 367–375 (1987)
Grauman, K., Darrell, T.: Pyramid matching kernel: Discriminative classification with sets of image features. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 2, pp. 1458–1465 (2005)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006, vol. 2, pp. 2169–2178 (2006)
Byun, H.-R., Lee, S.-W.: Applications of support vector machines for pattern recognition: A survey. In: Lee, S.-W., Verri, A. (eds.) SVM 2002. LNCS, vol. 2388, pp. 213–236. Springer, Heidelberg (2002)
Teague, M.R.: Image analysis via the general theory of moments. Optical Society of America 70(8), 920–930 (1979)
Vorobyov, M.: Shape Classification Using Zernike Moments. Technical Report. iCamp-University of California Irvine (2011)
Lowe, D.: Distinctive image features from scale-invariant keypoints. In: IJCV (2004)
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Lozano-Vega, G., Benezeth, Y., Marzani, F., Boochs, F. (2013). Classification of Pollen Apertures Using Bag of Words. In: Petrosino, A. (eds) Image Analysis and Processing – ICIAP 2013. ICIAP 2013. Lecture Notes in Computer Science, vol 8156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41181-6_72
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DOI: https://doi.org/10.1007/978-3-642-41181-6_72
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