A fuzzy clustering algorithm to detect planar and quadric shapes

In this paper, we introduce a new fuzzy clustering algorithm to detect an unknown number of planar and quadric shapes in noisy data. The proposed algorithm is computationally and implementationally simple, and it overcomes many of the drawbacks of the existing algorithms that have been proposed for similar tasks. Since the clustering is performed in the original image space, and since no features need to be computed, this approach is particularly suited for sparse data. The algorithm may also be used in pattern recognition applications

Confidence-Guided Data Augmentation for Deep Semi-Supervised Training

We propose a new data augmentation technique for semi-supervised learning settings that emphasizes learning from the most challenging regions of the feature space. Starting with a fully supervised reference model, we first identify low confidence predictions. These samples are then used to train a Variational AutoEncoder (VAE) that can generate an infinite number of additional images with similar distribution. Finally, using the originally labeled data and the synthetically generated labeled and unlabeled data, we retrain a new model in a semi-supervised fashion. We perform experiments on two benchmark RGB datasets: CIFAR-100 and STL-10, and show that the proposed scheme improves classification performance in terms of accuracy and robustness, while yielding comparable or superior results with respect to existing fully supervised approachesComment: 7 page

Abstract Interactive image retrieval using fuzzy sets

We present an image retrieval system which permits the user to submit a coarse initial query and continuously re®ne it. The user's relevance feedbacks is modeled by fuzzy sets, and is used to discover and use the more discriminatory features for the given query. The proposed system uses a dissimilarity measure based on the fuzzy integral. Ó 2001 Elsevier Science B.V. All rights reserved

Unsupervised Learning of Prototypes and AttributeWeights

In this paper, we introduce new algorithms that perform clustering and feature weighting simultaneously and in an unsupervised manner. The proposed algorithms are computationally and implementation ally simple, and learn a different set of feature weights for each identified cluster. The cluster dependent feature weights offer two advantages. First, they guide the clustering process to partition the data set into more meaningful clusters. Second, they can be used in the subsequent steps of a learning system to improve its learning behavior. An extension of the algorithm to deal with an unknown number of clusters is also proposed. The extension is based on competitive agglomeration, whereby the number of clusters is over specified, and adjacent clusters are allowed to compete for data points in a manner that causes clusters which lose in the competition to gradually become depleted and vanish. We illustrate the performance of the proposed approach by using it to segment color images, and to build a nearest prototype classifier