Interval Valued Feature Selection for Classification of Logo Images
A model for classification of logo images through a symbolic feature selection is proposed in this paper. The proposed model extracts three global features viz., color, texture, and shape from logo images. These features are then fused to emphasize the superiority of feature level fusion strategy. Due to the existence of large variations across the samples in each class, the samples are clustered and represented in the form of symbolic interval valued data during training. The symbolic feature selection is then adopted to show the efficacy of feature sub-setting in classifying the logo images. During testing, a query logo image is classified as one of the members of three classes with only few discriminable set of features using a suitable symbolic classifier. For experimentation purpose, a huge corpus of 5044 color logo images has been used. The proposed model is validated using suitable validity measures viz., f-measure, precision, recall, accuracy, and time. The results with the comparative analysis show the superiority of the symbolic feature selection method with that of without feature selection in terms of time and average f-measure.
KeywordsLogo image K-Means clustering Symbolic feature selection Symbolic classification
The second author would like to acknowledge the Department of Science Technology, INDIA, for their financial support through DST-INSPIRE fellowship.
- 1.Hesson, A., Androutsos, D.: Logo and trademark detection in images using color wavelet co-occurrence histograms. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2008, pp.1233–1236 (2008)Google Scholar
- 2.Nourbakhsh, F., Karatzas, D., Valveny, E.: A polar-based logo representation based on topological and colour features. In: Proceedings of the 9th IAPR Workshop on Document Analysis Systems, pp. 341–348. ACM Press (2010)Google Scholar
- 3.Romberg, S., Pueyo, L.G., Lienhart, R., Zwol, R.V.: Scalable logo recognition in real-world images. In: ACM International Conference on Multimedia Retrieval 2011, ICMR 2011 (2011)Google Scholar
- 4.Kalantidis, Y., Pueyo, L.G., Trevisiol, M., Zwol, R.V., Avrithis, Y.: Scalable triangulation-based logo recognition. In: ACM International Conference on Multimedia Retrieval, ICMR 2011 (2011)Google Scholar
- 5.Romberg, S., Lienhart, R.: Bundle min-hashing for logo recognition. In: Proceedings of the 3rd ACM International Conference on Multimedia Retrieval, ICMR 2013, pp. 113–120 (2013)Google Scholar
- 8.Arafat, Y.S., Saleem, M., Hussain, A.S.: Comparative analysis of invariant schemes for logo classification. In: International Conference on Emerging Technologies, pp. 256–261 (2009)Google Scholar
- 10.Guru, D.S., Kumar, N.V.: Symbolic representation and classification of logos. In: Proceedings of International Conference on Computer Vision and Image Processing (CVIP-2016). AISC Series, vol. 459, pp 555–569. Springer, Singapore (2016)Google Scholar
- 12.Guru, D.S., Kumar, N.V.: Class specific feature selection for interval valued data through interval K-Means clustering. In: RTIP2R 2016. CCIS, vol. 709, pp. 228–239. Springer, Singapore (2017)Google Scholar
- 13.Ichino, M.: Feature selection for symbolic data classification. In: New Approaches in Classification and Data Analysis, pp. 423–429. Springer, Heidelberg (1994)Google Scholar
- 14.Kiranagi, B.B., Guru D.S., Ichino M.: Exploitation of multivalued type proximity for symbolic feature selection. In: Proceedings of the Internal Conference on Computing: Theory and Applications, pp. 320–324. IEEE (2007)Google Scholar
- 16.Guru, D.S., Kumar, N.V.: Novel feature ranking criteria for interval valued feature selection. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 149–155. IEEE (2016)Google Scholar
- 19.Barros, A.P., Carvalho, F.A.T., Neto, E.A.L.: A pattern classifier for interval-valued data based on multinomial logistic regression model. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 541–546 (2012)Google Scholar