Unsupervised and Semi-Supervised Clustering for Large Image Database Indexing and Retrieval

• Published in: 2012 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future pp. 1 - 6
• Main Authors: Lai Hien Phuong, Visani, M., Boucher, A., Ogier, J.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-02-2012
• Subjects: Clustering algorithms; Clustering methods; Context; Image databases; Indexes; Vectors
• ISBN: 1467303070; 9781467303071
• DOI: 10.1109/rivf.2012.6169869

The feature space structuring methods play a very important role in finding information in large image databases. They organize indexed images in order to facilitate, accelerate and improve the results of further retrieval. Clustering, one kind of feature space structuring, may organize the dataset into groups of similar objects without prior knowledge (unsupervised clustering) or with a limited amount of prior knowledge (semi- supervised clustering). In this paper, we present both formal and experimental comparisons of different unsupervised clustering methods for structuring large image databases. We use different image databases of increasing sizes (Wang, PascalVoc2006, Caltech101, Core130k) to study the scalability of the different approaches. Moreover, a summary of semi-supervised clustering methods is presented and an interactive semi-supervised clustering model using the HMRF-kmeans is experimented on the Wang image database in order to analyse the improvement of the clustering results when user feedbacks are provided.