Sabri Boutemedjet

A Hybrid Feature Extraction Selection Approach for High-Dimensional Non-Gaussian Data Clustering

This paper presents an unsupervised approach for feature selection and extraction in mixtures of generalized Dirichlet (GD) distributions. Our method defines a new mixture model that is able to extract independent and non-Gaussian features without loss of accuracy. The proposed model is learned using the expectation-maximization algorithm by minimizing the message length of the data set. Experimental results show the merits of the proposed methodology in the categorization of object images.

A Graphical Model for Context-Aware Visual Content Recommendation

Existing recommender systems provide an elegant solution to the information overload in current digital libraries such as the Internet archive. Nowadays, the sensors that capture the users contextual information such as the location and time are become available and have raised a need to personalize recommendations for each user according to his/her changing needs in different contexts. In addition, visual documents have richer textual and visual information that was not exploited by existing recommender systems. In this paper, we propose a new framework for context-aware recommendation of visual documents by modeling the user needs, the context and also the visual document collection together in a unified model. We address also the users need for diversified recommendations. Our pilot study showed the merits of our approach in content based image retrieval.

Image and Video Segmentation by Combining Unsupervised Generalized Gaussian Mixture Modeling and Feature Selection

In this letter, we propose a clustering model that efficiently mitigates image and video under/over-segmentation by combining generalized Gaussian mixture modeling and feature selection. The model has flexibility to accurately represent heavy-tailed image/video histograms, while automatically discarding uninformative features, leading to better discrimination and localization of regions in high-dimensional spaces. Experimental results on a database of real-world images and videos showed us the effectiveness of the proposed approach.

A hybrid probabilistic framework for content-based image retrieval with feature weighting

In this paper, we propose a probabilistic framework for efficient retrieval and indexing of image collections. This framework uncovers the hierarchical structure underlying the collection from image features based on a hybrid model that combines both generative and discriminative learning. We adopt the generalized Dirichlet mixture and maximum likelihood for the generative learning in order to estimate accurately the statistical model of the data. Then, the resulting model is refined by a new discriminative likelihood that enhances the power of relevant features. Consequently, this new model is suitable for modeling high-dimensional data described by both semantic and low-level (visual) features. The semantic features are defined according to a known ontology while visual features represent the visual appearance such as color, shape, and texture. For validation purposes, we propose a new visual feature which has nice invariance properties to image transformations. Experiments on the Microsofts collection (MSRCID) show clearly the merits of our approach in both retrieval and indexing.

A finite mixture model for simultaneous high-dimensional clustering, localized feature selection and outlier rejection

Model-based approaches and in particular finite mixture models are widely used for data clustering which is a crucial step in several applications of practical importance. Indeed, many pattern recognition, computer vision and image processing applications can be approached as feature space clustering problems. For complex high-dimensional data, however, the use of these approaches presents several challenges such as the presence of many irrelevant features which may affect the speed and also compromise the accuracy of the used learning algorithm. Another problem is the presence of outliers which potentially influence the resulting models parameters. For this purpose, we propose and discuss an algorithm that partitions a given data set without a priori information about the number of clusters, the saliency of the features or the number of outliers. We illustrate the performance of our approach using different applications involving synthetic data, real data and objects shape clustering.

Model-based subspace clustering of non-Gaussian data

This paper presents a new generalized Dirichlet (GD) mixture model to address the challenging problem of clustering multidimensional data sets on different feature subsets. We approximate class-conditional distributions of mixture components to define binary relevance of features at the level of clusters. We consider a relevant feature as the one providing the knowledge to assign data points in the cluster. Then, we define a new message length objective to learn the model and select both feature subsets and the number of components. The proposed method is general comparatively with existing feature selection and subspace clustering models. In addition, it selects for each cluster only relevant and statistically independent features in a linear time of the number of observations and dimensions. Experiments on synthetic data and in unsupervised image categorization show the merits of our approach.

A Graphical Model for Content Based Image Suggestion and Feature Selection

Content based image retrieval systems provide techniques for representing, indexing and searching images. They address only the users short term needs expressed as queries. From the importance of the visual information in many applications such as advertisements and security, we motivate in this paper, the Content Based Image Suggestion. It targets the users long term needs as a recommendation of products based on the user preferences in different situations, and on the visual content of images. We propose a generative model in which the visual features and users are clustered into separate classes. We identify the number of both user and image classes with the simultaneous selection of relevant visual features. The goal is to ensure an accurate prediction of ratings for multidimensional images. This model is learned using the minimum message length approach. Experiments with an image collection showed the merits of our approach.

Predictive Approach for User Long-Term Needs in Content-Based Image Suggestion

In this paper, we formalize content-based image suggestion (CBIS) as a Bayesian prediction problem. In CBIS, users provide the rating of images according to both their long-term needs and the contextual situation, such as time and place, to which they belong. Therefore, a CBIS model is defined to fit the distribution of the data in order to predict relevant images for a given user. Generally, CBIS becomes challenging when only a small amount of data is available such as in the case of “new users” and “new images.” The Bayesian predictive approach is an effective solution to such a problem. In addition, this approach offers efficient means to select highly rated and diversified suggestions in conformance with theories in consumer psychology. Experiments on a real data set show the merits of our approach in terms of image suggestion accuracy and efficiency.

Unsupervised feature and model selection for generalized Dirichlet mixture models

We present in this paper a new approach for unsupervised feature selection for non Gaussian data controlled by a finite mixture of generalized Dirichlet distributions. We model each feature by a mixture of two Beta distributions: one relevant and depends on component labels while the second distribution is uninformative for the clustering. The relevance of each feature is then quantified by the mixture weight associated to the relevant Beta distribution. Experiments in summarizing image collections have shown the merits of our approach.

Unsupervised Feature Selection and Learning for Image Segmentation

In this paper we investigate the integration of feature selection in segmentation through an unsupervised learning approach. We propose a clustering algorithm that efficiently mitigates image under/over-segmentation, by combining generalized Gaussian mixture modeling and feature selection. The algorithm is based on generalized Gaussian mixture modeling which is less prone to region number over-estimation in case of noisy and heavy-tailed image distributions. On the other hand, our feature selection mechanism allows to automatically discard uninformative features, which leads to better discrimination and localization of regions in high-dimensional spaces. Experimental results on a large database of real-world images showed us the effectiveness of the proposed approach.