@inproceedings{000d610ccde8421ba118a98317e69352,
title = "Image clustering using multi-visual features",
abstract = "This paper presents a research on clustering an image collection using multi-visual features. The proposed method extracted a set of visual features from each image and performed multi-dimensional K-Means clustering on the whole collection. Furthermore, this work experiments on different number of visual features combination for clustering. 2, 3, 5 and 7 pair of visual features chosen from a total of 8 visual features used, to measure the impact of using more visual features towards clustering performance. The result show that the accuracy of multi-visual features clustering is promising, but using too many visual features might set a drawback.",
keywords = "Image Clustering, K-Means Clustering, Visual Feature",
author = "Bilih Priyogi and Nungki Selviandro and Hasibuan, {Zainal A.} and Mubarik Ahmad",
year = "2014",
doi = "10.1007/978-3-642-55032-4_18",
language = "English",
isbn = "9783642550317",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "179--189",
booktitle = "Information and Communication Technology - Second IFIP TC5/8 International Conference, ICT-EurAsia 2014, Proceedings",
address = "Germany",
note = "2nd IFIP TC5/8 International Conference on Information and Communication Technology, ICT-EurAsia 2014 ; Conference date: 14-04-2014 Through 17-04-2014",
}