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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Chary, Sunitha, Lakshmi: Similar Image Searching from Image Database using Cluster Mean: Sorting and Performance Estimation (2012)
Maheshwari, Silakari, Motwani: Image Clustering using Color and Texture. In: First International Conference on Computational Intelligence, Communication Systems and Networks (2009)
Yildizier, Balci, Jarada, Alhajj: Integrating Wavelets with Clustering and Indexing for Effective Content-Based Image Retrieval (2011)
Lux, M., Chatzichristofis, S.A.: Lire: Lucene Image Retrieval – An Extensible Java CBIR Library. In: Proceedings of the 16th ACM International Conference on Multimedia, Vancouver, Canada, pp. 1085–1088 (2008)
Huang, J., Zabih, R.: Combining Color and Spatial Information for Content-based Image Retrieval. Cornell University, New York (1998)
Chatzichristofis, S.A., Boutalis, Y.S.: CEDD: Color and Edge Directivity Descriptor, a Compact Descriptor for Image Indexing and Retrieval. Democritus University of Thrace, Xanthi (2008)
Ohm, Cieplinski, Kim, Krishnamachari, Manjunath, Messing, Yamada: The MPEG-7 Color Descriptors (2001)
Won, C.S., et al.: Efficient Use of MPEG-7 Edge Histogram Descriptor. ETRI Journal 24(1), 23–30 (2002)
Chatzichristofis, S.A., Boutalis, Y.S.: FCTH: Fuzzy Color and Texture Histogram, a Low Level Feature for Accurate Image Retrieval. Democritus University of Thrace, Xanthi (2008b)
Yang, Y., Newsam, S.: Comparing SIFT Descriptors and Gabor Texture Features for Classification of Remote Sense Imagery. In: Proceedings of ICIP (2008)
Tamura, H., Mori, S., Yamawaki, T.: Textual Features Corresponding to Visual Perception. IEEE Transactions on Systems, Man, and Cybernetics, 460–473 (1978)
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Priyogi, B., Selviandro, N., Hasibuan, Z.A., Ahmad, M. (2014). Image Clustering Using Multi-visual Features. In: Linawati, Mahendra, M.S., Neuhold, E.J., Tjoa, A.M., You, I. (eds) Information and Communication Technology. ICT-EurAsia 2014. Lecture Notes in Computer Science, vol 8407. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55032-4_18
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DOI: https://doi.org/10.1007/978-3-642-55032-4_18
Publisher Name: Springer, Berlin, Heidelberg
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