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Instance segmentation is an approach that identifies, for every pixel, the specific belonging instance of the object. It detects each distinct object of interest in the image. [19] For example, when each person in a figure is segmented as an individual object. Panoptic segmentation combines both semantic and instance segmentation. Like semantic ...
The semantic link network was systematically studied as a semantic social networking method. Its basic model consists of semantic nodes, semantic links between nodes, and a semantic space that defines the semantics of nodes and links and reasoning rules on semantic links. The systematic theory and model was published in 2004. [20]
Image analysis is a typical domain for which a high degree of abstraction from low-level methods is required, and where the semantic gap immediately affects the user. If image content is to be identified to understand the meaning of an image, the only available independent information is the low-level pixel data.
Objects detected with OpenCV's Deep Neural Network module (dnn) by using a YOLOv3 model trained on COCO dataset capable to detect objects of 80 common classes. Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. [1]
In machine learning, semantic analysis of a text corpus is the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents. Semantic analysis strategies include: Metalanguages based on first-order logic, which can analyze the speech of humans.
It uses machine learning techniques to create a semantic interpreter, which extracts text fragments from articles into a sorted list. The fragments are sorted by how related they are to the surrounding text. Latent semantic analysis (LSA) is another common method that does not use ontologies, only considering the text in the input space.
Semantic queries work on named graphs, linked data or triples. This enables the query to process the actual relationships between information and infer the answers from the network of data . This is in contrast to semantic search , which uses semantics (meaning of language constructs) in unstructured text to produce a better search result.
ESA (explicit semantic analysis) based on Wikipedia and the ODP; SSA (salient semantic analysis) [48] which indexes terms using salient concepts found in their immediate context. n° of Wikipedia (noW), [49] inspired by the game Six Degrees of Wikipedia, [50] is a distance metric based on the hierarchical structure of Wikipedia.