Categorization of Similar Objects Using Bag of Visual Words and k - Nearest Neighbour Classifier

• Autorzy: Artiemjew P. , Górecki P. , Sopyła K.
• Języki publikacji: EN

Abstrakty

EN

Image categorization is one of the fundamental tasks in computer vision, it has wide application in methods of artificial intelligence, robotic vision and many others. There are a lot of difficulties in computer vision to overcome, one of them appears during image recognition and classification. The difficulty arises from an image variance, which may be caused by scaling, rotation, changes in a perspective, illumination levels, or partial occlusions. Due to these reasons, the main task is to represent represent images in such way that would allow recognizing them even if they have been modified. Bag of Visual Words (BoVW) approach, which allows for describing local characteristic features of images, has recently gained much attention in the computer vision community. In this article we have presented the results of image classification with the use of BoVW and k - Nearest Neighbor classifier with different kinds of metrics and similarity measures. Additionally, the results of k - NN classification are compared with the ones obtained from a Support Vector Machine classifier.

Słowa kluczowe

EN

Image categorization; k-Nearest Neighbor Classifier; Bag of Visual Words

PL

kategoryzacja obrazu; metoda k najbliższych sąsiadów; zbiór słów wizualnych

• Wydawca: Wydawnictwo Uniwersytetu Warmińsko-Mazurskiego w Olsztynie
• Czasopismo: Technical Sciences / University of Warmia and Mazury in Olsztyn
• Rocznik: 2012
• Tom: nr 15(2)
• Strony: 293--305
• Opis fizyczny: Bibliogr. 20 poz., rys., tab., wykr.

Twórcy

autor

Artiemjew P.

autor

Górecki P.

autor

Sopyła K.

• Chair of Mathematical Methods in Computer Science, University of Warmia and Mazury Olsztyn,

Bibliografia

• ALAHI A., ORTIZ R. VANDERGHEYNST P. 2012. FREAK: Fast Retina Keypoint. IEEE Conference on Computer Vision and Pattern Recognition, Rhode Island, Providence, USA.
• BAY H., TUYTELAARS T., VAN GOOL L. 2006. Surf: Speeded up robust features. ECCV, p. 404-417.
• CHAPELLE O., HAFFNER P., VAPNIK V.N. 1999. Support vector machines for histogram-based image classification. Neural Networks, IEEE Transactions on, 10(5): 1055-1064.
• CSURKA G., DANCE CH.R., FAN L., WILLAMOWSKI J., BRAY C. 2004. Visual categorization with bags of keypoints. Workshop on Statistical Learning in Computer Vision, ECCV, p. 1-22.
• DESELAERS T., PIMENIDIS L., NEY H. 2008. Bag-of-visual-words models for adult image classification and filtering. ICPR, p. 1-4.
• DEZA E., DEZA M. 2009. Encyclopedia of Distances, Springer.
• FAN R., CHEN P., LIN Ch. 2005. Working set selection using the second order information for training svm. Journal of machine learning research, 6: 1889-1918.
• GÓRECKI P., ARTIEMJEW P., DROZDA P., SOPYŁA K. 2012a. Categorization of Similar Objects using Bag of Visual Words and Support Vector Machines. Fourth International Conference on Agents and Artificial Intelligence. IEEE.
• GÓRECKI P., SOPYŁA K., DROZDA P. 2012b. Ranking by k-means voting algorithm for similar image retrieval. In: ICAISC 1(7267): 509-517, of Lecture Notes in Computer Science, eds. L. Rutkowski, M. Korytkowski, R. Scherer, R. Tadeusiewicz, L.A. Zadeh, J.M. Zurada, Springer.
• GÓRECKI P., SOPYŁA K., DROZDA P. 2012c. Different SVM Kernels for Bag of Visual Words. In: International Congress of Young Scientist, SMI’2012, Springer.
• HERBRICH R., GRAEPEL T., OBERMAYER K. 2000. Large margin rank boundaries for ordinal regression. MIT Press, Cambridge, MA.
• LEUTENEGGER S., CHLI M., SIEGWART R. 2011. BRISK: Binary Robust Invariant Scalable Keypoints. Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2548-2555.
• LEWIS D.D. 1998. Naive (bayes) at forty: The independence assumption in information retrieval. Springer Verlag, p. 4-15.
• LOWE D.G. 2004. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision, 60: 91-110.
• MAK M.W., GUO J., KUNG S.-Y. 2008. Pairprosvm: Protein subcellular localization based on local pairwise profile alignment and svm. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 5(3): 416-422.
• MIKOLAJCZYK K., LEIBE B., SCHIELE B. 2005. Local Features for Object Class Recognition. Tenth IEEE International Conference on Computer Vision (ICCV’05), 1: 1792-1799.
• MOLINARO A.M., SIMON R., PFEIFFER R.M. 2005. Prediction error estimation. a comparison of resampling methods, Bioinformatics, 21(15): 3301-3307.
• NISTR D., STEWNIUS H. 2006. Scalable recognition with a vocabulary tree. In IN CVPR, p. 2161-2168.
• THORSTEN J. 1998. Text categorization with support vector machines: learning with many relevant features. Proceedings of ECML-98, 10th European Conference on Machine Learning, 1398: 137-142. Eds. C. N’edellec, C. Rouveirol. Springer Verlag, Heidelberg, DE.
• TUYTELAARS T., MIKOLAJCZYK K. 2008. Local invariant feature detectors: a survey. Found. Trends. Comput. Graph. Vis., 3: 177-280.