Temu Kembali Citra Berbasis Konten Pada Citra Lintas Domain

Yaya Wihardi, Herbert Siregar, Ali Mulyawan


This research aimed to develop a cross domain content based image retrieval system by using Histogram of Oriented Gradient (HoG) features. To compute visual similarity, we use Gaussian Approximation for Fast Image Similarity (GAFIS), Online Algorithm for Scalable Image Similarity Learning (OASIS), and Hybrid method that combine both of GAFIS and OASIS. The results show that the hybrid method outperforms the others, it is because the methods could extract uniqueness of each image query and image database by utilizing Gaussian parameters from negative-set and combine it with OASIS parameters

Keywords : Image Retrieval, CBIR, Gaussian Approximation, OASIS.

Full Text:

PDF Remote PDF


Atta, R., Joshi, D. D., Li, J., & Wang, J. Z. (2008). Image Retrieval: Ideas, Inflluences, and Trends of the New Age. ACM Transactions on Computing Surveys.

Bar-Hillel, A., Hertz, T., Shental, N., & Weinshall, D. (2003). Learning Distance Functions using Equivalence Relations. Proc. of 20th International Conference on Machine Learning (ICML), (pp. 11–18).

Bay, H., Tuytelaars, T., & Gool, L. V. (2008, June). Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding, 110(3), 346-359 .

Chechik, G., Sharma, V., Shalit, U., & Bengio, S. (2009). An Online Algorithm for Large Scale Image Similarity Learning. Advances in Neural Information Processing Systems.

Dalal, N., & Triggs, B. (2005). Histograms of Oriented Gradients for Human Detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (pp. 886 - 893). San Diego.

Everingham, M., Van~Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (n.d.). The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results. Retrieved from http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html

Gharbi, M., Malisiewicz, T., Paris, S., & Durand, F. (2012). A Gaussian Approximation of Feature Space for Fast Image Similarity. Massachusett: DSpace@MIT.

Lowe, D. G. (2004). Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 60(2), 91-110.

Shrivastava, A., Malisiewicz, T., Gupta, A., & Efros, A. A. (2011). Data-driven visual similarity for cross-domain image matching. Proceedings of the 2011 SIGGRAPH Asia Conference. Hong Kong.

Weinberger, K., Blitzer, J., & Saul, L. (2006). Distance metric learning for large margin nearest neighbor classification. NIPS, 18.

Wol, L., Hassner, T., & Taigman, Y. (2009). The One-Shot similarity kernel . IEEE International Conference on Computer Vision 12th, (pp. 897 - 902). Kyoto.

Xing, E., Ng, A., Jordan, M., & Russell, S. (2003). Distance metric learning with application to clustering with side information. NIPS, 15, 521–528.


  • There are currently no refbacks.

Copyright (c) 2018

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.