A survey of image labelling for computer vision applications
- URL: http://arxiv.org/abs/2104.08885v1
- Date: Sun, 18 Apr 2021 16:01:55 GMT
- Title: A survey of image labelling for computer vision applications
- Authors: Christoph Sager, Christian Janiesch, Patrick Zschech
- Abstract summary: Recent rise of deep learning algorithms for recognising image content has led to the emergence of ad-hoc labelling tools.
We perform a structured literature review to compile the underlying concepts and features of image labelling software.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Supervised machine learning methods for image analysis require large amounts
of labelled training data to solve computer vision problems. The recent rise of
deep learning algorithms for recognising image content has led to the emergence
of many ad-hoc labelling tools. With this survey, we capture and systematise
the commonalities as well as the distinctions between existing image labelling
software. We perform a structured literature review to compile the underlying
concepts and features of image labelling software such as annotation
expressiveness and degree of automation. We structure the manual labelling task
by its organisation of work, user interface design options, and user support
techniques to derive a systematisation schema for this survey. Applying it to
available software and the body of literature, enabled us to uncover several
application archetypes and key domains such as image retrieval or instance
identification in healthcare or television.
Related papers
- Pixels to Prose: Understanding the art of Image Captioning [1.9635669040319872]
Image captioning enables machines to interpret visual content and generate descriptive text.
The review traces the evolution of image captioning models to the latest cutting-edge solutions.
The paper also delves into the application of image captioning in the medical domain.
arXiv Detail & Related papers (2024-08-28T11:21:23Z) - Revolutionizing Text-to-Image Retrieval as Autoregressive Token-to-Voken Generation [90.71613903956451]
Text-to-image retrieval is a fundamental task in multimedia processing.
We propose an autoregressive voken generation method, named AVG.
We show that AVG achieves superior results in both effectiveness and efficiency.
arXiv Detail & Related papers (2024-07-24T13:39:51Z) - An Image-based Typology for Visualization [23.716718517642878]
We present and discuss the results of a qualitative analysis of visual representations from images.
We derive a typology of 10 visualization types of defined groups.
We provide a dataset of 6,833 tagged images and an online tool that can be used to explore and analyze the large set of labeled images.
arXiv Detail & Related papers (2024-03-07T04:33:42Z) - Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey [49.47197748663787]
This review aims to provide a first comprehensive and organized overview of the state-of-the-art research results on pseudo-label methods in the field of semi-supervised semantic segmentation.
In addition, we explore the application of pseudo-label technology in medical and remote-sensing image segmentation.
arXiv Detail & Related papers (2024-03-04T10:18:38Z) - InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision Generalists [66.85125112199898]
We develop a unified language interface for computer vision tasks that abstracts away task-specific design choices.
Our model, dubbed InstructCV, performs competitively compared to other generalist and task-specific vision models.
arXiv Detail & Related papers (2023-09-30T14:26:43Z) - Enhancing Textbooks with Visuals from the Web for Improved Learning [50.01434477801967]
In this paper, we investigate the effectiveness of vision-language models to automatically enhance textbooks with images from the web.
We collect a dataset of e-textbooks in the math, science, social science and business domains.
We then set up a text-image matching task that involves retrieving and appropriately assigning web images to textbooks.
arXiv Detail & Related papers (2023-04-18T12:16:39Z) - Automatic Image Content Extraction: Operationalizing Machine Learning in
Humanistic Photographic Studies of Large Visual Archives [81.88384269259706]
We introduce Automatic Image Content Extraction framework for machine learning-based search and analysis of large image archives.
The proposed framework can be applied in several domains in humanities and social sciences.
arXiv Detail & Related papers (2022-04-05T12:19:24Z) - Label Assistant: A Workflow for Assisted Data Annotation in Image
Segmentation Tasks [0.8135412538980286]
We propose a generic workflow to assist the annotation process and discuss methods on an abstract level.
Thereby, we review the possibilities of focusing on promising samples, image pre-processing, pre-labeling, label inspection, or post-processing of annotations.
In addition, we present an implementation of the proposal by means of a developed flexible and extendable software prototype nested in hybrid touchscreen/laptop device.
arXiv Detail & Related papers (2021-11-27T19:08:25Z) - Computer Vision for Supporting Image Search [2.18624447693809]
We leverage the benefits of huge amounts of data available for training, we have enormous computer processing available and we have seen the evolution of machine learning as a suite of techniques to process data and deliver accurate vision-based systems.
We use this in autonomous vehicle navigation or in security applications, searching CCTV for example, and in medical image analysis for healthcare diagnostics.
One application which is not widespread is image or video search directly by users. In this paper we present the need for such image finding or re-finding by examining human memory and when it fails, thus motivating the need for a different approach to image search which is
arXiv Detail & Related papers (2021-11-16T20:50:32Z) - Image Segmentation Using Deep Learning: A Survey [58.37211170954998]
Image segmentation is a key topic in image processing and computer vision.
There has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models.
arXiv Detail & Related papers (2020-01-15T21:37:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.