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Unsupervised and Interactive Semi-supervised Clustering for Large Image Database Indexing and Retrieval

Lai H. P., Visani M., Boucher A., Ogier J. M.

Fundamenta Informaticae

2014

Vol. 130, nr 2

201--218

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, Corel30k) to study the scalability of the different approaches. Then, we present a new interactive semi-supervised clustering model, which allows users to provide feedback in order to improve the clustering results according to their wishes. Moreover,we also compare, experimentally, our proposed model with the semi-supervised HMRF-kmeans clustering method.

An Improved Cop-Kmeans Clustering for Solving Constraint Violation Based on MapReduce Framework

Yang Y., Rutayisire T., Lin C., Li C., Teng F.

Fundamenta Informaticae

2013

Vol. 126, nr 4

301--318

Clustering with pairwise constraints has received much attention in the clustering community recently. Particularly, must-link and cannot-link constraints between a given pair of instances in the data set are common prior knowledge incorporated in many clustering algorithms today. This approach has been shown to be successful in guiding a number of famous clustering algorithms towards more accurate results. However, recent work has also shown that the incorporation of must-link and cannot-link constraints makes clustering algorithms too much sensitive to “the assignment order of instances” and therefore results in consequent constraint violation. The major contributions of this paper are two folds. One is to address the issue of constraint violation in Cop-Kmeans by emphasizing a sequenced assignment of cannot-link instances after conducting a Breadth-First Search of the cannot-link set. The other is to reduce the computational complexity of Cop-Kmeans for massive data sets by adopting a MapReduce Framework. Experimental results show that our approach performs well on massive data sets while may overcome the problem of constraint violation.