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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 ...
A semantic data model in software engineering has various meanings: It is a conceptual data model in which semantic information is included. This means that the model describes the meaning of its instances. Such a semantic data model is an abstraction that defines how the stored symbols (the instance data) relate to the real world. [1]
Saliency estimation may be viewed as an instance of image segmentation. In computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as superpixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more ...
The semantic gap characterizes the difference between two descriptions of an object by different linguistic representations, for instance languages or symbols. According to Andreas M. Hein, the semantic gap can be defined as "the difference in meaning between constructs formed within different representation systems". [ 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.
Some variations of formal semantics include the following: Action semantics [9] is an approach that tries to modularize denotational semantics, splitting the formalization process in two layers (macro and microsemantics) and predefining three semantic entities (actions, data and yielders) to simplify the specification;
Semantic intelligence [1] is the ability to gather the necessary information to allow to identify, detect and solve semantic gaps on all level of the organization.. Similar to Operational intelligence or Business Process intelligence, which aims to identify, detect and then optimize business processes, semantic intelligence targets information instead of processes.
A semantic network may be instantiated as, for example, a graph database or a concept map. Typical standardized semantic networks are expressed as semantic triples. Semantic networks are used in neurolinguistics and natural language processing applications such as semantic parsing [2] and word-sense disambiguation. [3]