A Guide To Different Types Of Annotations And When To Use Each

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A Guide To Different Types Of Annotations And
When To Use Each
From logistics to IT and healthcare to retail,
fully leveraging your organizations data can be
challenging. Fundamentally, data is required for
comprehending and interpreting your reality.
Data, as a result of an action, as a set of
digital media, as the knowledge generated from a
model, or as information retrieved from a sensor
must be appropriately accessed, interpreted,
prioritized, and leveraged to improve your
business results.
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Data annotation helps a Machine Learning (ML)
algorithm identify the data it processes and
determine its context. Thus, in computer science,
annotation is a process of information labeling
and tagging a given scenario with a special
purpose for a given model. In 2022, the data
annotation market was valued at 805.6 million
its projected to cross the 5.3 billion mark by
the end of 2030. Within this market, there are
different methodologies of annotation catering to
the diversity of data being generated online.
Though the guiding factors for selecting
appropriate annotation modes vary from project to
project, there are some broad categories to
consider to determine which ones are best suited
for your next challenge. This article seeks to
provide a guide to various annotation methods
based on various data types and when each should
be used. 1. Textual Or Linguistic
Annotation Textual annotation, within the ML
context, helps develop metadata models and
ontologies that focus on the extraction of
lexical and semantic information from large
volumes of data and assist in developing a text
corpus. Some of the more common types of textual
annotation involve parsing, sentence
segmentation, chunking, named entity recognition,
named entities extraction, part of speech
tagging, lemmatization, parsing-surface
realization relation identification, phonetic
annotation, semantic role labeling,
problem-oriented tagging, and discoursal
annotation. An example of linguistic annotation
could be the labeling of tweets to establish the
sentiment expressed, thus providing a
categorization of their types (e.g., positive,
negative, or neutral). In such a scenario, the
data must reside in a structured format that is
sensitive to the semantic content and properly
handles various language modalities (e.g.,
English, German, French).
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• This process is made easier with the help of
  text-processing tools. It also depends upon the
  dataset being accessed.
• When To Use Textual Annotation?
• In general, textual annotation is employed for
  the extraction of lexical and semantic
  information from large volumes of text. The
  following are some common scenarios that
  necessitate the use of linguistic metadata
  generation.
• When you are dealing with time-sensitive
  information such as tweets, chat logs, and news
  stories.
• When you want to develop a text corpus and
  related metadata models for NLP applications or
  question-answering systems or when
  developing automatic summarization algorithms
  that provide summaries as answers to specific
  questions.
• When you want to develop monitoring systems in a
  given domain in a particular language.
• When you need to develop a text- or
  document-level data mining system.
• From a strictly linguistic perspective, textual
  annotation is used to generate metadata that
  corresponds to semantic categories of
  words/phrases, anaphoric links, thought
  presentation, the identity of a word,
  pronunciation of a word, etc.
• 2. Image Annotation
• In computer vision, image annotation involves
  marking key points and regions of interest on a
  given image. It is a process of tagging visual
  media with a visual cue for a given model to
  interpret. Some of the annotation types include
  fixed-point detection, segmentation, object
  recognition, region clustering, etc.

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• An example of image annotation could be the
  labeling of objects in a scene, with emphasis on
  the identification of faces and human body parts.
  In such a scenario, it is critical that the
  annotator can precisely identify the salient
  features of the image being processed or
  supervised, thus allowing it to be used for
  training a model.
• When To Use Image Annotation?
• In technical terms, image annotation is used for
  landmarking, bounding boxes, transcription, and
  pixel-level labeling. From a business
  perspective, the following are the scenarios that
  require the use of image annotation
• When you want to develop a human-oriented
  surveillance system, for example, for tracking
  individuals or monitoring the movement of
  vehicles.
• When you want to create a traffic sign detection
  system.
• When you need to provide labeling for atlases and
  manuals, with emphasis on the identification of
  objects about a particular topic.
• When you need to project the image for a computer
  graphics application, etc.
• When you want to develop a self-driving car that
  recognizes and responds to objects detected by
  its cameras.
• 3. Video Annotation
• Video annotation, within the computer vision
  context, is employed for marking key points on a
  given video that can eventually be used to
  generate metadata about the content of the video.
  Some of the types of annotation include key-frame
  detection, structural segmentation, object
  detection, object recognition, etc.

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• An example of video annotation could be labeling
  a scene for a given service to recognize which
  objects are present and what actions they take.
  Such a case would demand the use of a combination
  of gazetteers and human-generated metadata, which
  would serve as training data for an automated
  model.
• When To Use Video Annotation?
• Video annotations technicalities encompass
  bounding 2D and 3D boxes, conceptualizing
  polygons, landmarking, and drawing lines and
  splines, among others. From the application
  perspective, video annotation can be used
• For AR/VR content-based applications, identifying
  where to place virtual objects
• For video-based applications, tagging key events
  and interactions with the people in the scene
• For surveillance purposes, such as monitoring
  security cameras
• For spatial reasoning tasks like identifying
  point clouds and points of interest in a given
  scene
• For video evaluation, such as for face detection
  and recognition, tracking entities or events that
  appear to be of interest over time, etc.
• 4. Object Detection
• A computer vision technique, object detection is
  used in digital image processing to identify
  objects in images or videos. As a key output of
  deep learning and machine learning algorithms,
  object detection helps spot people, objects,
  scenes, and visual details in images or videos.
• The goal of object detection is to teach a
  computer what comes naturally to humans to gain
  a decent level of understanding of what an image
  contains. As a subset of object recognition, it
  helps in identifying an object and also locating
  it in the image or video.

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• When To Use Object Detection?
• Object detection plays a key role in
  driverless cars, disease identification,
  industrial inspection, and more. Here are some
  use cases
• When you want to detect people in the image or
  video streams as part of video surveillance
• When you want to analyze how or which aisles
  people shop in a store for customer needs
  analysis
• When you want to count animals on a farm or check
  the cause of damaged produce
• When you want to check the number plates of
  suspicious vehicles, say at an airport or a
  restricted industrial setting.
• Semantic Segmentation
• As a deep learning algorithm, semantic
  segmentation associates a label with every pixel
  in an image. It works to recognize a collection
  of pixels that form distinctive categories. As
  compared to object detection, where objects have
  to fit a bounding box, semantic segmentation
  viably detects irregularly shaped objects.
• When To Use Semantic Segmentation?
• Semantic segmentations labeling capabilities
  make it a perfect choice for applications in a
  variety of industries that require precise image
  maps. Here are some scenarios where semantic
  segmentation can be used
• When you want to identify and navigate
  objects, for example, in autonomous vehicles
  to separate the road from obstacles such as
  vehicles, pedestrians, sidewalks, traffic light
  signals, etc.
• When you want to detect defects in materials such
  as manufacturing equipment.
• When you want to identify terrains such as
  mountains, rivers, fields, or deserts via
  satellite imagery.

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• When you want to analyze and detect
  medical conditions, such as identifying
  cancerous anomalies in cells.
• Instance Segmentation
• A special type of image segmentation,
  instance segmentation detects and segments every
  object in an image, even if multiple objects of
  the same class are present. In the sphere of
  computer vision, it helps in segregating
  instances of objects in a complex visual
  environment and in demarcating their boundaries.
  Unlike semantic segmentation, which cannot
  differentiate the same objects in one image as
  different, instance segmentation will do it
  seamlessly.
• When To Use Instance Segmentation
• Instance segmentation is particularly useful when
  distinct objects of related types are present and
  need to be monitored separately. Popular use
  cases include
• When you want to have a detailed understanding of
  your surroundings with pixel-level accuracy, for
  example, in a self-driving car.
• When you want to segregate objects from one
  another, say, cargo ships from passenger ships
  for maritime security purposes.
• When you want to categorize items, for example,
  clothing in a retail store.
• Panoptic Segmentation
• Panoptic segmentation is a type of image
  annotation that combines the prediction from
  semantic segmentation and instance segmentation
  into a unified output. As the name suggests, it
  analyzes everything visible in a given visual
  field while also identifying things like
  background and unannotated objects and
  holistically generalizes the task of image
  segmentation.

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• In computer vision, the task of panoptic
  segmentation can be broken down into three basic
  steps separating each object in the image into
  individual parts, labeling each separated part,
  and classifying them.
• When To Use Panoptic Segmentation?
• Since panoptic segmentation identifies objects
  according to class labels and instances in any
  given image, it is used across various
  applications, such as
• When you are dealing with large volumes of visual
  data that is difficult to interpret, say while
  recognizing tumor cells.
• When you want to identify and classify objects in
  an image, for example, a traffic surveillance
  camera to determine the cause of an accident.
• When you want to simultaneously detect countable
  objects with different backgrounds, say in an
  urban setting.
• Keypoint Annotation
• The keypoint annotation takes a detailed approach
  to image annotation and is used to detect small
  objects and shape variations. You can use key
  point annotation to label a single pixel in an
  image and portray an objects shape.
• When To Use Keypoint Annotation?
• Keypoint annotations are well-suited for tracking
  the movements of objects, people, or animals.
  Popular use cases include
• When you want to track variations between objects
  that have the same structure, for example, human
  or facial features.
• When you want to analyze the performance of
  players by tracking and analyzing performance
  improvements not visible to the human eye.
• When you want to track or analyze human poses,
  say in an AR/ VR application.

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• When you want to detect the hand movements of
  workers, say in a manufacturing setup.
• When you want to track the movement of livestock
  on a farm.
• Multi-Label Classification
• Multi-label classification allows you to assign
  multiple labels or classes to a single image.
  With the large surge in digital images, this type
  of classification allows for an efficient way to
  analyze, annotate, and manipulate image data.
• Unlike traditional classification, which involves
  predicting a single label, multi-label
  classification involves predicting the likelihood
  across two or more class labels that are mutually
  exclusive. This means the classification task
  assumes the input belongs to a single class only.
• When To Use Multi-label Classification?
• Multi-label classification is becoming
  increasingly popular due to the increasing number
  of different real-world application domains, such
  as
• When you want to categorize text documents.
• When you want to carry out a detailed medical
  diagnosis.
• When you want to categorize music to unearth the
  underlying emotion.
• Conclusion
• The ubiquitous nature of data has created the
  need for automated information discovery systems
  capable of achieving enhanced accuracy and
  precision to cater to ever-growing intrinsic
  business complexity. Considering that,
  information scientists and researchers who engage
  in such practices must be familiar with all the
  schemes of annotation and the related processes
  that accompany them.

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As such, it is necessary to consider the various
forms of annotation and then experiment with
the numerous features and options available
and select techniques that best suit any given
situation. Connect with EnFuse Solutions to scale
and optimize the data annotation processes. Read
More Key Skills That Data Annotation Experts
Must Possess