Abstract
We propose a bag of constrained visual words model for image representation. Each image under this model is considered to be an aggregation of patches. SURF features are used to describe each patch. Two sets of constraints, namely, the must-link and the cannot-link, are developed for each patch in a completely unsupervised manner. The constraints are formulated using the distance information among different patches as well as statistical analysis of the entire patch data. All the patches from the image set under consideration are then quantized using the Linear-time-Constrained Vector Quantization Error (LCVQE), a fast yet accurate constrained k-means algorithm. The resulting clusters, which we term as constrained visual words, are then used to label the patches in the images. In this way, we model an image as a bag (histogram) of constrained visual words and then show its utility for image retrieval. Clustering as well as initial retrieval results on COIL-100 dataset indicate the merit of our approach.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Sivic, J., Zisserman, A.: Video Google: efficient visual search of videos. In: Toward Category-Level Object Recognition, pp. 127–144 (2006)
Sivic, J., Zisserman, A.: Video Google: a text retrieval approach to object matching in videos. In: Proceedings of the ICCV, pp. 470–1477 (2003)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Hartigan, J.A., Wong, M.A.: A k-means clustering algorithm. Appl. Stat. 28, 100–108 (1979)
Bouachir, W., Kardouchi, M., Belacel, N.: Improving bag of visual words image retrieval: a fuzzy weighting scheme for efficient indexation. In: Proceedings of the SITIS, pp. 215–220 (2009)
Mukherjee, A., Chakraborty, S., Sil, J., Chowdhury, A.S.: A novel visual word assignment model for content based image retrieval. In: Balasubramanian, R., et al. (eds.) Proceedings of the CVIP, Springer AISC, vol. 459, pp. 79–87 (2016)
Dimitrovski, I., Kocev, D., Loskovska, S., Dzeroski, S.: Improving bag-of-visual-words image retrieval with predictive clustering trees. Inf. Sci. 329(2), 851–865 (2016)
Fu, H., Qiu, G.: Fast semantic image retrieval based on random forest. In: Proceedings of the ACM MM, pp. 909–912 (2012)
Mukherjee, A., Sil, J., Chowdhury, A.S.: Image retrieval using random forest based semantic similarity measures and SURF based visual words. In: Chaudhuri, B.B., et al. (eds.) Proceedings of the CVIP, Springer AISC, vol. 703, pp. 79–90 (2017)
Pelleg, D., Baras, D.: K-means with large and noisy constraint sets. In: Proceedings of the ECML, pp. 674–682 (2007)
Nene, S.A., Nayar, S.K., Murase, H.: Columbia Object Image Library (COIL-100), Tech. Report, Department of Computer Science, Columbia University CUCS-006-96 (1996)
Zhang, X., et al.: Spatially constrained bag-of-visual-words for hyperspectral image classification. In: Proceedings of the IEEE IGARSS, pp. 501–504 (2016)
Davidson, I., Ravi, S.S.: Clustering with constraints: feasibility issues and the k-means algorithm. In: 5th SIAM Data Mining Conference (2005)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 2, 224–227 (1979)
Calinski, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. 3(1), 1–27 (1974)
Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: ideas, influences, and trends of the new age. ACM Comput. Surv. 40(2), 1–60 (2008)
Newsam, S., Yang Y.: Comparing global and interest point descriptors for similarity retrieval in remote sensed imagery. In: Proceedings of the ACM GIS, Article No. 9 (2007)
Wan, J., et al.: Deep learning for content-based image retrieval: a comprehensive study. In: Proceedings of the ACM MM, pp. 157–166 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mukherjee, A., Sil, J., Chowdhury, A.S. (2020). A Bag of Constrained Visual Words Model for Image Representation. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1024. Springer, Singapore. https://doi.org/10.1007/978-981-32-9291-8_32
Download citation
DOI: https://doi.org/10.1007/978-981-32-9291-8_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-32-9290-1
Online ISBN: 978-981-32-9291-8
eBook Packages: EngineeringEngineering (R0)