Abstract
In this paper, we propose a novel image retrieval scheme using random forest-based semantic similarity measures and SURF-based bag of visual words. A patch-based representation for the images is carried out with SURF-based bag of visual words. A random forest, which is an ensemble of randomized decision trees, is applied next on a set of training images. The training images accumulate into different leaf nodes in each decision tree of the random forest as a result. During retrieval, a query image, represented using SURF-based bag of visual words, is passed through each decision tree. We define a query path and a semantic neighbor set for such query images in all the decision trees. Different measures of semantic image similarity are derived by exploring the characteristics of query paths and semantic neighbor sets. Experimental results on the publicly available COIL-100 image database clearly demonstrate the superior performance of the proposed content-based image retrieval (CBIR) method with these new measures over some of the similar existing approaches.
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References
Datta, R., Joshi, D., Li, J., Wang, James Z., Image retrieval: Ideas, influences, and trends of the new age, ACM Computing Surveys, 40(2), 1–60, (2008).
Sivic, J., Zisserman, A.: Video Google: Efficient Visual Search of Videos, In Toward Category-Level Object Recognition, 127–144, (2006).
Bay, H., Ess, A., Tuytelaars, T., and Van Gool, L.: Speeded-up robust features (SURF), Computer Vision and Image Understanding, 110(3), 346–359, (2008).
Lowe D. G.: Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, 60(2), 91–110, (2004).
Bouachir, W., Kardouchi, M., Belacel, N.: Improving Bag of Visual Words Image Retrieval: A Fuzzy Weighting Scheme for Efficient Indexation, Proc. SITIS, 215–220, (2009).
Mukherjee, A., Chakraborty, S., Sil, J., Chowdhury, A.S.: A Novel Visual Word Assignment Model for Content Based Image Retrieval, Proc. CVIP, Balasubramanian Raman et al. (eds.), Springer AISC, Vol. 459, 79–87, (2016).
Rahman, M.M., Bhattacharya, P., Kamel, M., Campilho A.: Probabilistic Similarity Measures in Image Databases with SVM Based Categorization and Relevance Feedback, Proc. ICIAR, Springer LNCS, Vol. 3656, 601–608, (2005).
Liu Y., Zhang D., Lu G., Ma W-Y.: A survey of content-based image retrieval with high-level semantics, Pattern Recognition, 40(1), 262–282, (2007).
Fu, H., Qiu G.: Fast Semantic Image Retrieval Based on Random Forest, Proc. ACM MM, 909–912, (2012).
Moosman, F., Triggs, B. and Jurie, F.: Fast Discriminative Visual Codebooks using Randomized Clustering Forests, Proc. NIPS, 985–992, (2006).
Dimitrovski, I., Kocev, D., Loskovska, S., Dzeroski, S.: Improving bag-of-visual-words image retrieval with predictive clustering trees, Information Science, 329(2), 851–865, (2016).
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).
Breiman, L.: Random Forests, Machine Learning, 45, 5–32, (2001).
Sivic, J., Zisserman A.: Video Google: A Text Retrieval Approach to Object Matching in Videos, Proc. ICCV, 470–1477, (2003).
Newsam, S., Yang Y.: Comparing global and interest point descriptors for similarity retrieval in remote sensed imagery, Proc. ACM GIS, Article No. 9, (2007).
Kontschieder P., Rota Bulo S., Pelillo M.: Semantic Labeling and Object Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(10), 2104–2116, (2014).
Wan J. et al.: Deep Learning for Content-Based Image Retrieval: A Comprehensive Study, Proc. ACM MM, 157–166, (2014).
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Mukherjee, A., Sil, J., Chowdhury, A.S. (2018). Image Retrieval Using Random Forest-Based Semantic Similarity Measures and SURF-Based Visual Words. In: Chaudhuri, B., Kankanhalli, M., Raman, B. (eds) Proceedings of 2nd International Conference on Computer Vision & Image Processing . Advances in Intelligent Systems and Computing, vol 703. Springer, Singapore. https://doi.org/10.1007/978-981-10-7895-8_7
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DOI: https://doi.org/10.1007/978-981-10-7895-8_7
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