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.
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Bilih Priyogi, Nungki Selviandro, Zainal Hasibuan, Mubarik Ahmad. Image Clustering Using Multi-visual Features. 2nd Information and Communication Technology - EurAsia Conference (ICT-EurAsia), Apr 2014, Bali, Indonesia. pp.179-189, ⟨10.1007/978-3-642-55032-4_18⟩. ⟨hal-01397191⟩

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