Add to ORKGSupport Vector Machines can be coupled with Bayesian methods. This allows us to find categories and to perform semantic learning
A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation ...
Several combinations of the preprocessing algorithms, feature selection techniques and classifiers c...
Learning Bayesian Belief Networks (BBN) from corpora and incorporating the extracted inferring knowl...
We present adfvanced concepts how a search engine for a satellite image database can be designed
Support vector machines (SVMs) are popular classification tools. An SVM can be enhanced via a Bayesi...
Abstract — In this paper, a learning approach coupling Support Vector Machines (SVMs) and a Genetic ...
In this paper, a learning approach to semantic image analysis and classification is proposed that co...
automatically segments the dominant foreground region, consisting of the semantic concept of the ima...
Support vector machines (SVMs) are a family of machine learning methods, originally introduced for t...
In this paper, we propose a support vector machines (SVMs) method of classifying image regions hiera...
In content-based image retrieval, the “semantic gap ” between visual image features and user semanti...
The support vector machine (SVM) is widely used for machine learning and artificial intelligence. Tr...
This book is the first comprehensive introduction to Support Vector Machines (SVMs), a new generatio...
• Matching Strategy – Literal term matching (matching word patterns between the query and documents)...
Image retrieval using multiple features often uses explicit weights that represent the importance of...
A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation ...
Several combinations of the preprocessing algorithms, feature selection techniques and classifiers c...
Learning Bayesian Belief Networks (BBN) from corpora and incorporating the extracted inferring knowl...
We present adfvanced concepts how a search engine for a satellite image database can be designed
Support vector machines (SVMs) are popular classification tools. An SVM can be enhanced via a Bayesi...
Abstract — In this paper, a learning approach coupling Support Vector Machines (SVMs) and a Genetic ...
In this paper, a learning approach to semantic image analysis and classification is proposed that co...
automatically segments the dominant foreground region, consisting of the semantic concept of the ima...
Support vector machines (SVMs) are a family of machine learning methods, originally introduced for t...
In this paper, we propose a support vector machines (SVMs) method of classifying image regions hiera...
In content-based image retrieval, the “semantic gap ” between visual image features and user semanti...
The support vector machine (SVM) is widely used for machine learning and artificial intelligence. Tr...
This book is the first comprehensive introduction to Support Vector Machines (SVMs), a new generatio...
• Matching Strategy – Literal term matching (matching word patterns between the query and documents)...
Image retrieval using multiple features often uses explicit weights that represent the importance of...
A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation ...
Several combinations of the preprocessing algorithms, feature selection techniques and classifiers c...
Learning Bayesian Belief Networks (BBN) from corpora and incorporating the extracted inferring knowl...