Ouiem Bchir

Piecewise Convex Multiple-Model Endmember Detection and Spectral Unmixing

A hyperspectral endmember detection and spectral unmixing algorithm that finds multiple sets of endmembers is presented. Hyperspectral data are often nonconvex. The Piecewise Convex Multiple-Model Endmember Detection algorithm accounts for this using a piecewise convex model. Multiple sets of endmembers and abundances are found using an iterative fuzzy clustering and spectral unmixing method. The results indicate that the piecewise convex representation estimates endmembers that better represent hyperspectral imagery composed of multiple regions where each region is represented with a distinct set of endmembers.

Endoscopy video summarization based on unsupervised learning and feature discrimination

We propose a novel endoscopy video summarization approach based on unsupervised learning and feature discrimination. The proposed learning approach partitions the collection of video frames into homogeneous categories based on their visual and temporal descriptors. Also, it generates possibilistic memberships in order to represent the degree of typicality of each video frame within every category, and reduce the influence of noise frames on the learning process. The algorithm learns iteratively the optimal relevance weight for each feature subset within each cluster. Moreover, it finds the optimal number of clusters in an unsupervised and efficient way by exploiting some properties of the possibilistic membership function. The endoscopy video summary consists of the most typical frames in all clusters after discarding noise frames. We compare the performance of the proposed algorithm with state-of-the-art learning approaches. We show that the possibilistic approach is more robust. The endoscopy videos collection includes more than 90k video frames.

Multiple model endmember detection based on spectral and spatial information

We introduce a new spectral mixture analysis approach. Unlike most available approaches that only use the spectral information, this approach uses the spectral and spatial information available in the hyperspectral data. Moreover, it does not assume a global convex geometry model that encompasses all the data but rather multiple local convex models. Both the multiple model boundaries and the models endmembers and abundances are fuzzy. This allows points to belong to multiple groups with different membership degrees. Our approach is based on minimizing a joint objective function to simultaneously learn the underling fuzzy multiple convex geometry models and find a robust estimate of the models endmembers and abundances.

A comparison of deterministic and probabilistic approaches to endmember representation

The piece-wise convex multiple model endmember detection algorithm (P-COMMEND) and the Piece-wise Convex End-member detection (PCE) algorithm autonomously estimate many sets of endmembers to represent a hyperspectral image. A piece-wise convex model with several sets of endmembers is more effective for representing non-convex hyperspectral imagery over the standard convex geometry model (or linear mixing model). The terms of the objective function in P-COMMEND are based on geometric properties of the input data and the endmember estimates. In this paper, the P-COMMEND algorithm is extended to autonomously determine the number of sets of endmembers needed. The number of sets of endmembers, or convex regions, is determined by incorporating the competitive agglomeration algorithm into P-COMMEND. Results are shown comparing the Competitive Agglomeration P-COMMEND (CAP) algorithm to results found using the statistical PCE endmember detection method.

An Overview of Unsupervised and Semi-Supervised Fuzzy Kernel Clustering

For real-world clustering tasks, the input data is typically not easily separable due to the highly complex data structure or when clusters vary in size, density and shape. Kernel-based clustering has proven to be an effective approach to partition such data. In this paper, we provide an overview of several fuzzy kernel clustering algorithms. We focus on methods that optimize an fuzzy C-mean-type objective function. We highlight the advantages and disadvantages of each method. In addition to the completely unsupervised algorithms, we also provide an overview of some semi-supervised fuzzy kernel clustering algorithms. These algorithms use partial supervision information to guide the optimization process and avoid local minima. We also provide an overview of the different approaches that have been used to extend kernel clustering to handle very large data sets.

Generic Evaluation Metrics for Hyperspectral Data Unmixing

AbstractWe propose novel generic performance metric for hyperspectral unmixing techniques. This relative metric compares two abundance matrices. The first one represents the unmixing result. The second matrix can be either another unmixing result or the ground truth of the hyperspectral scene. This metric starts by computing coincidence matrices corresponding to the two abundance matrices, then the comparison is carried out by computing statistics of the number of pairs of data points that have high abundances with respect to the same endmember for the first unmixing approach, but have large abundance differences with respect to the same endmember for the second unmixing technique, or large differences in both. The main advantage of this metric approach is that there is no need to pair the endmembers of the two unmixing approaches. Rather, it assumes that the pixels, which are considered as different/same material by one unmixing approach should also be considered different/same material by the other. Our...

Insult Detection in Social Network Comments Using Possibilistic Based Fusion Approach

This paper aims to propose a novel approach to automatically detect verbal offense in social network comments. It relies on a local approach that adapts the fusion method to different regions of the feature space in order to classify comments from social networks as insult or not. The proposed algorithm is formulated mathematically through the minimization of some objective function. It combines context identification and multi-algorithm fusion criteria into a joint objective function. This optimization is intended to produce contexts as compact clusters in subspaces of the high-dimensional feature space via possibilistic unsupervised learning and feature weighting. Our initial experiments have indicated that the proposed fusion approach outperforms individual classifiers and the global fusion method. Also, in order to validate the obtained results, we compared the performance of the proposed approach with related fusion methods.

Mixture analysis based on spectral summarization

We introduce a new spectral mixture analysis approach. The proposed Mixture Analysis based on Spectral Summarization (MASS) uses all the wavelengths of the hyperspectral image and assumes a convex geometry model in order to estimate the endmembers and their corresponding abundances. MASS unmixing technique is based on the information provided by the summarization of the hyperspectral image. The summarization is performed through a fuzzy partitioning of the hyperspectral scene.

Fuzzy clustering with Learnable Cluster dependent Kernels

We propose a new relational clustering approach, called Fuzzy clustering with Learnable Cluster dependent Kernels (FLeCK), that learns multiple kernels while seeking compact clusters. A Gaussian kernel is learned with respect to each cluster. It reflects the relative density, size, and position of the cluster with respect to the other clusters. These kernels are learned by optimizing both the intra-cluster and the inter-cluster similarities. Moreover, FLeCK is formulated to work on relational data. This makes it applicable to data where objects cannot be represented by vectors or when clusters of similar objects cannot be represented efficiently by a single prototype. The experiments show that FLeCK outperforms several other algorithms. In particular, we show that when data include clusters with various inter and intra cluster distances, learning cluster dependent kernel is crucial in obtaining a good partition.

Fuzzy relational kernel clustering with Local Scaling Parameter Learning

We introduce a new fuzzy relational clustering technique with Local Scaling Parameter Learning (LSPL). The proposed approach learns the underlying cluster dependent dissimilarity measure while finding compact clusters in the given data set. The learned measure is a Gaussian similarity function defined with respect to each cluster that allows to control the scaling of the clusters and thus, improve the final partition. We minimize one objective function for both the optimal partition and for the cluster dependent scaling parameter. This optimization is done iteratively by dynamically updating the partition and the scaling parameter in each iteration. This makes the proposed algorithm simple and fast. Moreover, as we assume that the data is available in a relational form, the proposed approach is applicable even when only the degree to which pairs of objects in the data are related is available. It is also more practical when similar objects cannot be represented by a single prototype.