A generative model for spatial color image databases categorization

Bouguila, Nizar; ElGuebaly, Walid

doi:10.1109/icassp.2008.4517736

Cited by 21 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An important problem is the categorization of this multimedia content which is typically noisy and generally described in the form of high-dimensional feature vectors [46]. In this first application, we focus on the problem of image categorization which has several potential applications and can be used for image databases browsing, content-based image categorization, retrieval and suggestion [47], [48], [49], [50], [51], [52]. We follow the same categorization methodology previously proposed in [13] by using adopting the bag of visual words formalism which has been widely used recently for this task.…”

Section: A Visual Scenes Categorizationmentioning

confidence: 99%

Count Data Clustering Using Unsupervised Localized Feature Selection and Outliers Rejection

Bouguila

2011

2011 IEEE 23rd International Conference on Tools With Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

This paper presents an unsupervised statistical model for simultaneous clustering, feature selection and outlier rejection in the case of count data. The proposed model is based on a finite discrete mixture to which a uniform component is added to ensure robustness to outliers and noise. The consideration of a finite mixture model is justified by its flexibility, its solid grounding in the theory of statistics and its competitive results. We derive a complete maximum a posteriori learning approach that does not require a priori knowledge about the number of outliers and the number of clusters. A rigorous expectation maximization (EM) algorithm, based on the formulation of a maximum a posteriori (MAP) estimation, is also provided. We report experimental results of applying our model to the challenging problems of visual scenes categorization and texture discrimination.

show abstract

Section: A Visual Scenes Categorizationmentioning

confidence: 99%

Count Data Clustering Using Unsupervised Localized Feature Selection and Outliers Rejection

Bouguila

2011

2011 IEEE 23rd International Conference on Tools With Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore two images can have similar colour distribution but great difference in visual sense. The general idea in using CH is to divide an image into sub-areas and calculate a histogram for each of these sub-areas [9]. Increasing the number of sub-areas leads to increase the usage of memory and computational time.…”

Section: Introductionmentioning

confidence: 99%

Content-based image retrieval using the combination of the fast wavelet transformation and the colour histogram

Singha

Hemachandran

Paul

2012

IET Image Processing

View full text Add to dashboard Cite

In this study, an attempt has been made to study an image retrieval technique based on the combination of Haar wavelet transformation using lifting scheme and the colour histogram (CH) called lifting wavelet-based colour histogram. The colour feature is described by the CH, which is translation and rotation invariant. The Haar wavelet transformation is used to extract the texture features and the local characteristics of an image, to increase the accuracy of the retrieval system. The lifting scheme reduces the processing time to retrieve images. The experimental results indicate that the proposed technique outperforms the other schemes, in terms of the average precision, the average recall and the total average precision/recall.

show abstract

“…Data classification problems arise in many different applications from many domains such as computer vision, data mining and knowledge discovery, image processing, and pattern recognition [1], [2], [3], [4], [5]. Among classification approaches, perhaps the most widely used and studied approaches recently have been based on SVM [6], [7], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

A model-based discriminative framework for sets of positive vectors classification: Application to object categorization

Bouguila

2014

2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

Self Cite

View full text Add to dashboard Cite

Classic support vector machines (SVM) kernels (e.g. polynomial, Gaussian) are unable to take advantage of existing problem-specific knowledge. This is especially true when the problem at hand is the classification of sets (or bags) of vectors that may represent textual or visual (e.g. images, videos) data. This article tackles the problem of the classification of sets of positive vectors via SVM. It describes approaches to generate SVM kernels, appropriate for this problem, from generalized inverted Dirichlet (GID) mixtures. We develop several kernels using the fact that the GID belongs to the exponential family of distributions. The promise of such an approach and its advantages are demonstrated via a challenging application that concerns object categorization.

show abstract

A generative model for spatial color image databases categorization

Cited by 21 publications

References 11 publications

Count Data Clustering Using Unsupervised Localized Feature Selection and Outliers Rejection

Count Data Clustering Using Unsupervised Localized Feature Selection and Outliers Rejection

Content-based image retrieval using the combination of the fast wavelet transformation and the colour histogram

A model-based discriminative framework for sets of positive vectors classification: Application to object categorization

Contact Info

Product

Resources

About