Pengenalan Gambar Tempat Wisata Dengan Deep Local Feature Dan Support Vector Machine

Angelika Dibijo, Agustinus Noertjahyana, Alvin Nathaniel Tjondrowiguno


Saving moment at a place usually done with photos. However, due to the large number of photos, organizing photos becomes difficult. A tourist may not know the name of the tourist spot he visited, do not have time to name the photo or forget the name of the place the photo was taken. Manually searching for places names through photos will take a long time.

This research will conduct a trial with the implementation of the Deep Local Feature (DELF) method and Support Vector Machine (SVM) to recognize photos of tourist attractions automatically. The DELF method is an effective method for capturing image features, especially place pictures. After capturing image features, the images will be grouped based on features with SVM.

The test is carried out to get the value of the parameter taking features with DELF and classification with SVM so that the recognition of tourist attractions has a high level of accuracy. For 153 image classes, DELF is performed with an image threshold of 50 and a max feature of 1000. While the classification uses SVM with kernel rbf with cost 10 and gamma 0.01. By using the DELF and SVM obtained accuracy with a test data of 0.6178.


Image Recognition; Deep Local Feature; Support Vector Machine

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Azhar, R., Tuwohingide, D., & Kamudi, D. 2015. Batik Image Classification Using SIFT Feature Extraction , Bag of Features and Support Vector Machine. Procedia - Procedia Computer Science, 72, 24–30. DOI=

Cao, J., Zhao, Y., Lai, X., Ong, M. E. H., Yin, C., Koh, Z. X., & Liu, N. 2015. Landmark recognition with sparse representation classification and extreme learning machine. Journal of the Franklin Institute, 352(10), 4528–4545. DOI=

He, K., Zhang, X., Ren, S., & Sun, J. 2016. Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 770–778. DOI=

Long, J., Shelhamer, E., & Darrell, T. (n.d.). Fully Convolutional Networks for Semantic Segmentation.

Lowe, D. G. 2004. Distinctive Image Features from Scale-Invariant Keypoints.

Noh, H., Araujo, A., Sim, J., Weyand, T., & Han, B. 2017. Large-Scale Image Retrieval with Attentive Deep Local Features. Proceedings of the IEEE International Conference on Computer Vision, 2017-Octob, 3476–3485. DOI=

Nugroho, A. S., Witarto, A. B., & Handoko, D. 2003. Support Vector Machine.

Umar, H. B. 2009. Principal Component Analysis (PCA) Dan Aplikasinya Dengan SPSS. Jurnal Kesehatan Masyarakat, Maret - September 2009, Vol. 03, No. 2.

Zheng, L., Yang, Y., & Tian, Q. 2015. SIFT Meets CNN : A Decade Survey of Instance Retrieval. 14(8)


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