The Experts below are selected from a list of 47286 Experts worldwide ranked by ideXlab platform
Akrem Sellami - One of the best experts on this subject based on the ideXlab platform.
-
Hyperspectral Imagery Semantic Interpretation Based on Adaptive Constrained Band Selection and Knowledge Extraction Techniques
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018Co-Authors: Akrem Sellami, Mohamed Farah, Imed Riadh Farah, Basel SolaimanAbstract:In this paper, we propose a novel adaptive band selection approach for hyperspectral image Semantic Interpretation. This approach is based on constrained band selection (CBS) method and extracted knowledge coming from tensor locality preserving projection. The extracted knowledge is presented as a set of rules which are used to evaluate the importance of spectral bands for classes discrimination. Based on these extracted rules and the CBS approach, relevant bands are selected to enhance the hyperspectral image Semantic Interpretation. The main advantage of the proposed adaptive band selection approach is to allow the automatic selection of discriminant, distinctive and informative spectral bands, and improve the Semantic Interpretation of hyperspectral images. Experimental results on real images show that the proposed band selection approach reaches competitive good performances, in terms of classification accuracy. Hyperspectral Imagery Semantic Interpretation Based on Adaptive Constrained Band... | Request PDF. Available from: https://www.researchgate.net/publication/323194459_Hyperspectral_Imagery_Semantic_Interpretation_Based_on_Adaptive_Constrained_Band_Selection_and_Knowledge_Extraction_Techniques [accessed Feb 16 2018].
-
Hyperspectral Imagery Semantic Interpretation Based on Adaptive Constrained Band Selection and Knowledge Extraction Techniques
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018Co-Authors: Akrem Sellami, Mohamed Farah, Imed Riadh Farah, Basel SolaimanAbstract:In this paper, we propose a novel adaptive band selection approach for hyperspectral image Semantic Interpretation. This approach is based on constrained band selection (CBS) method and extracted knowledge coming from tensor locality preserving projection. The extracted knowledge is presented as a set of rules which are used to evaluate the importance of spectral bands for classes discrimination. Based on these extracted rules and the CBS approach, relevant bands are selected to enhance the hyperspectral image Semantic Interpretation. The main advantage of the proposed adaptive band selection approach is to allow the automatic selection of discriminant, distinctive and informative spectral bands, and improve the Semantic Interpretation of hyperspectral images. Experimental results on real images show that the proposed band selection approach reaches competitive good performances, in terms of classification accuracy.
-
An Adaptive Dimensionality Reduction Approach for Hyperspectral Imagery Semantic Interpretation
International Science Index Medical and Health Science, 2017Co-Authors: Akrem Sellami, Imed Riadh Farah, Basel SolaimanAbstract:With the development of HyperSpectral Imagery (HSI) technology, the spectral resolution of HSI became denser, which resulted in large number of spectral bands, high correlation between neighboring, and high data redundancy. However, the Semantic Interpretation is a challenging task for HSI analysis due to the high dimensionality and the high correlation of the different spectral bands. In fact, this work presents a dimensionality reduction approach that allows to overcome the different issues improving the Semantic Interpretation of HSI. Therefore, in order to preserve the spatial information, the Tensor Locality Preserving Projection (TLPP) has been applied to transform the original HSI. In the second step, knowledge has been extracted based on the adjacency graph to describe the different pixels. Based on the transformation matrix using TLPP, a weighted matrix has been constructed to rank the different spectral bands based on their contribution score. Thus, the relevant bands have been adaptively selected based on the weighted matrix. The performance of the presented approach has been validated by implementing several experiments, and the obtained results demonstrate the efficiency of this approach compared to various existing dimensionality reduction techniques. Also, according to the experimental results, we can conclude that this approach can adaptively select the relevant spectral improving the Semantic Interpretation of HSI.
-
A Spatial Hypergraph Based Semi-Supervised Band Selection Method for Hyperspectral Imagery Semantic Interpretation
World Wide Web, 2016Co-Authors: Akrem Sellami, Imed Riadh FarahAbstract:Hyperspectral imagery (HSI) typically provides a wealth of information captured in a wide range of the electromagnetic spectrum for each pixel in the image. Hence, a pixel in HSI is a high-dimensional vector of intensities with a large spectral range and a high spectral resolution. Therefore, the Semantic Interpretation is a challenging task of HSI analysis. We focused in this paper on object classification as HSI Semantic Interpretation. However, HSI classification still faces some issues, among which are the following: The spatial variability of spectral signatures, the high number of spectral bands, and the high cost of true sample labeling. Therefore, the high number of spectral bands and the low number of training samples pose the problem of the curse of dimensionality. In order to resolve this problem, we propose to introduce the process of dimensionality reduction trying to improve the classification of HSI. The presented approach is a semi-supervised band selection method based on spatial hypergraph embedding model to represent higher order relationships with different weights of the spatial neighbors corresponding to the centroid of pixel. This semi-supervised band selection has been developed to select useful bands for object classification. The presented approach is evaluated on AVIRIS and ROSIS HSIs and compared to other dimensionality reduction methods. The experimental results demonstrate the efficacy of our approach compared to many existing dimensionality reduction methods for HSI classification.
Basel Solaiman - One of the best experts on this subject based on the ideXlab platform.
-
Hyperspectral Imagery Semantic Interpretation Based on Adaptive Constrained Band Selection and Knowledge Extraction Techniques
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018Co-Authors: Akrem Sellami, Mohamed Farah, Imed Riadh Farah, Basel SolaimanAbstract:In this paper, we propose a novel adaptive band selection approach for hyperspectral image Semantic Interpretation. This approach is based on constrained band selection (CBS) method and extracted knowledge coming from tensor locality preserving projection. The extracted knowledge is presented as a set of rules which are used to evaluate the importance of spectral bands for classes discrimination. Based on these extracted rules and the CBS approach, relevant bands are selected to enhance the hyperspectral image Semantic Interpretation. The main advantage of the proposed adaptive band selection approach is to allow the automatic selection of discriminant, distinctive and informative spectral bands, and improve the Semantic Interpretation of hyperspectral images. Experimental results on real images show that the proposed band selection approach reaches competitive good performances, in terms of classification accuracy. Hyperspectral Imagery Semantic Interpretation Based on Adaptive Constrained Band... | Request PDF. Available from: https://www.researchgate.net/publication/323194459_Hyperspectral_Imagery_Semantic_Interpretation_Based_on_Adaptive_Constrained_Band_Selection_and_Knowledge_Extraction_Techniques [accessed Feb 16 2018].
-
Hyperspectral Imagery Semantic Interpretation Based on Adaptive Constrained Band Selection and Knowledge Extraction Techniques
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018Co-Authors: Akrem Sellami, Mohamed Farah, Imed Riadh Farah, Basel SolaimanAbstract:In this paper, we propose a novel adaptive band selection approach for hyperspectral image Semantic Interpretation. This approach is based on constrained band selection (CBS) method and extracted knowledge coming from tensor locality preserving projection. The extracted knowledge is presented as a set of rules which are used to evaluate the importance of spectral bands for classes discrimination. Based on these extracted rules and the CBS approach, relevant bands are selected to enhance the hyperspectral image Semantic Interpretation. The main advantage of the proposed adaptive band selection approach is to allow the automatic selection of discriminant, distinctive and informative spectral bands, and improve the Semantic Interpretation of hyperspectral images. Experimental results on real images show that the proposed band selection approach reaches competitive good performances, in terms of classification accuracy.
-
An Adaptive Dimensionality Reduction Approach for Hyperspectral Imagery Semantic Interpretation
International Science Index Medical and Health Science, 2017Co-Authors: Akrem Sellami, Imed Riadh Farah, Basel SolaimanAbstract:With the development of HyperSpectral Imagery (HSI) technology, the spectral resolution of HSI became denser, which resulted in large number of spectral bands, high correlation between neighboring, and high data redundancy. However, the Semantic Interpretation is a challenging task for HSI analysis due to the high dimensionality and the high correlation of the different spectral bands. In fact, this work presents a dimensionality reduction approach that allows to overcome the different issues improving the Semantic Interpretation of HSI. Therefore, in order to preserve the spatial information, the Tensor Locality Preserving Projection (TLPP) has been applied to transform the original HSI. In the second step, knowledge has been extracted based on the adjacency graph to describe the different pixels. Based on the transformation matrix using TLPP, a weighted matrix has been constructed to rank the different spectral bands based on their contribution score. Thus, the relevant bands have been adaptively selected based on the weighted matrix. The performance of the presented approach has been validated by implementing several experiments, and the obtained results demonstrate the efficiency of this approach compared to various existing dimensionality reduction techniques. Also, according to the experimental results, we can conclude that this approach can adaptively select the relevant spectral improving the Semantic Interpretation of HSI.
Imed Riadh Farah - One of the best experts on this subject based on the ideXlab platform.
-
Hyperspectral Imagery Semantic Interpretation Based on Adaptive Constrained Band Selection and Knowledge Extraction Techniques
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018Co-Authors: Akrem Sellami, Mohamed Farah, Imed Riadh Farah, Basel SolaimanAbstract:In this paper, we propose a novel adaptive band selection approach for hyperspectral image Semantic Interpretation. This approach is based on constrained band selection (CBS) method and extracted knowledge coming from tensor locality preserving projection. The extracted knowledge is presented as a set of rules which are used to evaluate the importance of spectral bands for classes discrimination. Based on these extracted rules and the CBS approach, relevant bands are selected to enhance the hyperspectral image Semantic Interpretation. The main advantage of the proposed adaptive band selection approach is to allow the automatic selection of discriminant, distinctive and informative spectral bands, and improve the Semantic Interpretation of hyperspectral images. Experimental results on real images show that the proposed band selection approach reaches competitive good performances, in terms of classification accuracy. Hyperspectral Imagery Semantic Interpretation Based on Adaptive Constrained Band... | Request PDF. Available from: https://www.researchgate.net/publication/323194459_Hyperspectral_Imagery_Semantic_Interpretation_Based_on_Adaptive_Constrained_Band_Selection_and_Knowledge_Extraction_Techniques [accessed Feb 16 2018].
-
Hyperspectral Imagery Semantic Interpretation Based on Adaptive Constrained Band Selection and Knowledge Extraction Techniques
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018Co-Authors: Akrem Sellami, Mohamed Farah, Imed Riadh Farah, Basel SolaimanAbstract:In this paper, we propose a novel adaptive band selection approach for hyperspectral image Semantic Interpretation. This approach is based on constrained band selection (CBS) method and extracted knowledge coming from tensor locality preserving projection. The extracted knowledge is presented as a set of rules which are used to evaluate the importance of spectral bands for classes discrimination. Based on these extracted rules and the CBS approach, relevant bands are selected to enhance the hyperspectral image Semantic Interpretation. The main advantage of the proposed adaptive band selection approach is to allow the automatic selection of discriminant, distinctive and informative spectral bands, and improve the Semantic Interpretation of hyperspectral images. Experimental results on real images show that the proposed band selection approach reaches competitive good performances, in terms of classification accuracy.
-
An Adaptive Dimensionality Reduction Approach for Hyperspectral Imagery Semantic Interpretation
International Science Index Medical and Health Science, 2017Co-Authors: Akrem Sellami, Imed Riadh Farah, Basel SolaimanAbstract:With the development of HyperSpectral Imagery (HSI) technology, the spectral resolution of HSI became denser, which resulted in large number of spectral bands, high correlation between neighboring, and high data redundancy. However, the Semantic Interpretation is a challenging task for HSI analysis due to the high dimensionality and the high correlation of the different spectral bands. In fact, this work presents a dimensionality reduction approach that allows to overcome the different issues improving the Semantic Interpretation of HSI. Therefore, in order to preserve the spatial information, the Tensor Locality Preserving Projection (TLPP) has been applied to transform the original HSI. In the second step, knowledge has been extracted based on the adjacency graph to describe the different pixels. Based on the transformation matrix using TLPP, a weighted matrix has been constructed to rank the different spectral bands based on their contribution score. Thus, the relevant bands have been adaptively selected based on the weighted matrix. The performance of the presented approach has been validated by implementing several experiments, and the obtained results demonstrate the efficiency of this approach compared to various existing dimensionality reduction techniques. Also, according to the experimental results, we can conclude that this approach can adaptively select the relevant spectral improving the Semantic Interpretation of HSI.
-
A Spatial Hypergraph Based Semi-Supervised Band Selection Method for Hyperspectral Imagery Semantic Interpretation
World Wide Web, 2016Co-Authors: Akrem Sellami, Imed Riadh FarahAbstract:Hyperspectral imagery (HSI) typically provides a wealth of information captured in a wide range of the electromagnetic spectrum for each pixel in the image. Hence, a pixel in HSI is a high-dimensional vector of intensities with a large spectral range and a high spectral resolution. Therefore, the Semantic Interpretation is a challenging task of HSI analysis. We focused in this paper on object classification as HSI Semantic Interpretation. However, HSI classification still faces some issues, among which are the following: The spatial variability of spectral signatures, the high number of spectral bands, and the high cost of true sample labeling. Therefore, the high number of spectral bands and the low number of training samples pose the problem of the curse of dimensionality. In order to resolve this problem, we propose to introduce the process of dimensionality reduction trying to improve the classification of HSI. The presented approach is a semi-supervised band selection method based on spatial hypergraph embedding model to represent higher order relationships with different weights of the spatial neighbors corresponding to the centroid of pixel. This semi-supervised band selection has been developed to select useful bands for object classification. The presented approach is evaluated on AVIRIS and ROSIS HSIs and compared to other dimensionality reduction methods. The experimental results demonstrate the efficacy of our approach compared to many existing dimensionality reduction methods for HSI classification.
Udo Hahn - One of the best experts on this subject based on the ideXlab platform.
-
AIME - Semantic Interpretation of Medical Language - Quantitative Analysis and Qualitative Yield
Artificial Intelligence in Medicine, 2001Co-Authors: Martin Romacker, Udo HahnAbstract:We report on results from an empirical analysis of the Semantic Interpretation of medical free texts. Our approach to Semantic Interpretation is based on a lean collection of Interpretation rules which are triggered by well-defined configurations in dependency graphs in order to compute a conceptual representation of the texts' contents. We evaluate the accuracy of Semantic Interpretation for three types of syntactic dependency patterns, viz. genitives, auxiliary and modal verb complexes, and prepositional phrases. Besides quantitative considerations, we focus on the heuristic guidance, as provided by patterns underlying the Semantic Interpretation of prepositional phrases, for monitoring the quality of the medical domain knowledge base.
-
AMIA - Empirical data for the Semantic Interpretation of prepositional phrases in medical documents.
Proceedings. AMIA Symposium, 2001Co-Authors: Martin Romacker, Udo HahnAbstract:We report on the results from an empirical study deal-ing with the Semantic Interpretation of prepositional phrases in medical free texts. We use a small number of Semantic Interpretation schemata only, which operate on well-defined configurations in dependency graphs. We provide a quantitative analysis of the performance of the Semantic interpreter in terms of recall/precision data, and consider, in qualitative terms, the impact Semantic Interpretation patterns have on the construction of the underlying medical ontology.
-
ANLP - An empirical assessment of Semantic Interpretation
2000Co-Authors: Martin Romacker, Udo HahnAbstract:We introduce a framework for Semantic Interpretation in which dependency structures are mapped to conceptual representations based on a parsimonious set of Interpretation schemata. Our focus is on the empirical evaluation of this approach to Semantic Interpretation, i.e., its quality in terms of recall and precision. Measurements are taken with respect to two real-world domains, viz. information technology test reports and medical finding reports.
-
lean Semantic Interpretation
International Joint Conference on Artificial Intelligence, 1999Co-Authors: Martin Romacker, Katja Markert, Udo HahnAbstract:We introduce two abstraction mechanisms for streamlining the process of Semantic Interpretation. Configurational descriptions of dependency graphs increase the linguistic generality of Interpretation schemata, while interfacing them to lexical and conceptual inheritance hierarchies reduces the amount and complexity of Semantic specifications.
-
IJCAI - Lean Semantic Interpretation
1999Co-Authors: Martin Romacker, Katja Markert, Udo HahnAbstract:We introduce two abstraction mechanisms for streamlining the process of Semantic Interpretation. Configurational descriptions of dependency graphs increase the linguistic generality of Interpretation schemata, while interfacing them to lexical and conceptual inheritance hierarchies reduces the amount and complexity of Semantic specifications.
Raymond J. Mooney - One of the best experts on this subject based on the ideXlab platform.
-
Learning for Semantic Interpretation : Scaling up without dumbing down
Lecture Notes in Computer Science, 2000Co-Authors: Raymond J. MooneyAbstract:Most recent research in learning approaches to natural language have studied fairly low-level tasks such as morphology, part-of-speech tagging, and syntactic parsing. However, I believe that logical approaches may have the most relevance and impact at the level of Semantic Interpretation, where a logical representation of sentence meaning is important and useful. We have explored the use of inductive logic programming for learning parsers that map natural-language database queries into executable logical form. This work goes against the growing trend in computational linguistics of focusing on shallow but broad-coverage natural language tasks (scaling up by dumbing down) and instead concerns using logic-based learning to develop narrower, domain-specific systems that perform relatively deep processing. I first present a historical view of the shifting emphasis of research on various tasks in natural language processing and then briefly review our own work on learning for Semantic Interpretation. I will then attempt to encourage others to study such problems and explain why I believe logical approaches have the most to offer at the level of producing Semantic Interpretations of complete sentences.
-
Learning Language in Logic - Learning for Semantic Interpretation: scaling up without dumbing down
Learning Language in Logic, 2000Co-Authors: Raymond J. MooneyAbstract:Most recent researchin learning approaches to natural language have studied fairly "low-level" tasks such as morphology, part-ofspeechtagging, and syntactic parsing. However, I believe that logical approaches may have the most relevance and impact at the level of Semantic Interpretation, where a logical representation of sentence meaning is important and useful. We have explored the use of inductive logic programming for learning parsers that map natural-language database queries into executable logical form. This work goes against the growing trend in computational linguistics of focusing on shallow but broad-coverage natural language tasks ("scaling up by dumbing down") and instead concerns using logic-based learning to develop narrower, domain-specific systems that perform relatively deep processing. I first present a historical view of the shifting emphasis of research on various tasks in natural language processing and then briefly review our own work on learning for Semantic Interpretation. I will then attempt to encourage others to study such problems and explain why I believe logical approaches have the most to offer at the level of producing Semantic Interpretations of complete sentences.
