Semantic analysis of multimedia content is an on going research
area that has gained a lot of attention over the last few years.
Additionally, machine learning techniques are widely used for multimedia
analysis with great success. This work presents a combined approach
to Semantic adaptation of neural network classifiers in multimedia framework.
It is based on a fuzzy reasoning engine which is able to evaluate
the outputs and the confidence levels of the neural network classifier, using
a knowledge base. Improved image segmentation results are obtained,
which are used for adaptation of the network classifier, further increasing
its ability to provide accurate classification of the specific content.
This ECMA Standard specifies the form and establishes the interpretation of programs written in the C# programming language. It specifies
The representation of C# programs
The syntax and constraints of the C# language
The Semantic rules for interpreting C# programs
The restrictions and limits imposed by a conforming implementation of C#.
http://wordnet.princeton.edu/
WordNet is a large lexical database of English, developed under the direction of George A. Miller. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-Semantic and lexical relations. The resulting network of meaningfully related words and concepts can be navigated with the browser. WordNet is also freely and publicly available for download. WordNet s structure makes it a useful tool for computational linguistics and natural language processing.
AutoSummary uses Natural Language Processing to generate a contextually-relevant synopsis of plain text.
It uses statistical and rule-based methods for part-of-speech tagging, word sense disambiguation,
sentence deconstruction and Semantic analysis.
an approach for capturing similarity between words that was concerned with the syntactic similarity of two strings. Today we are back to discuss another approach that is more concerned with the meaning of words. Semantic similarity is a confidence score that reflects the Semantic relation between the meanings of two sentences. It is difficult to gain a high accuracy score because the exact Semantic meanings are completely understood only in a particular context.
In the previous article, we presented an approach for capturing similarity between words that was concerned with the syntactic similarity of two strings. Today we are back to discuss another approach that is more concerned with the meaning of words. Semantic similarity is a confidence score that reflects the Semantic relation between the meanings of two sentences. It is difficult to gain a high accuracy score because the exact Semantic meanings are completely understood only in a particular context.
This approach addresses two difficulties simultaneously: 1)
the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings in
monocular aerial images. With the suggested method building outlines can be detected
faster than the mobile robot can explore the area by itself, giving the robot an ability to
“see” around corners. At the same time, the approach can compensate for the absence
of elevation data in segmentation of aerial images. Our experiments demonstrate that
ground-level Semantic information (wall estimates) allows to focus the segmentation of
the aerial image to find buildings and produce a ground-level Semantic map that covers
a larger area than can be built using the onboard sensors.
We propose a technique that allows a person to design a new photograph
with substantially less effort. This paper presents a method that generates a composite image when a user types
in nouns, such as “boat” and “sand.” The artist can optionally design an intended image by specifying other
constraints. Our algorithm formulates the constraints as queries to search an automatically annotated image
database. The desired photograph, not a collage, is then synthesized using graph-cut optimization, optionally
allowing for further user interaction to edit or choose among alternative generated photos. An implementation of
our approach, shown in the associated video, demonstrates our contributions of (1) a method for creating specific
images with minimal human effort, and (2) a combined algorithm for automatically building an image library with
Semantic annotations from any photo collection.
