Use Image Clustering to Facilitate Technology Assisted Review
- URL: http://arxiv.org/abs/2112.08604v1
- Date: Thu, 16 Dec 2021 04:02:51 GMT
- Title: Use Image Clustering to Facilitate Technology Assisted Review
- Authors: Haozhen Zhao, Fusheng Wei, Hilary Quatinetz, Han Qin, Adam Dabrowski
- Abstract summary: Technology Assisted Review (TAR) in electronic discovery is witnessing a rising need to incorporate multimedia content in the scope.
We have developed innovative image analytics applications for TAR in the past years, such as image classification, image clustering, and object detection.
- Score: 0.5249805590164902
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: During the past decade breakthroughs in GPU hardware and deep neural networks
technologies have revolutionized the field of computer vision, making image
analytical potentials accessible to a range of real-world applications.
Technology Assisted Review (TAR) in electronic discovery though traditionally
has dominantly dealt with textual content, is witnessing a rising need to
incorporate multimedia content in the scope. We have developed innovative image
analytics applications for TAR in the past years, such as image classification,
image clustering, and object detection, etc. In this paper, we discuss the use
of image clustering applications to facilitate TAR based on our experiences in
serving clients. We describe our general workflow on leveraging image
clustering in tasks and use statistics from real projects to showcase the
effectiveness of using image clustering in TAR. We also summarize lessons
learned and best practices on using image clustering in TAR.
Related papers
- Exploiting CLIP-based Multi-modal Approach for Artwork Classification
and Retrieval [29.419743866789187]
We perform exhaustive experiments on the NoisyArt dataset which is a dataset of artwork images crawled from public resources on the web.
On such dataset CLIP achieves impressive results on (zero-shot) classification and promising results in both artwork-to-artwork and description-to-artwork domain.
arXiv Detail & Related papers (2023-09-21T14:29:44Z) - The Potential of Visual ChatGPT For Remote Sensing [0.0]
This paper examines the potential of Visual ChatGPT to tackle the aspects of image processing related to the remote sensing domain.
The model's ability to process images based on textual inputs can revolutionize diverse fields.
Although still in early development, we believe that the combination of LLMs and visual models holds a significant potential to transform remote sensing image processing.
arXiv Detail & Related papers (2023-04-25T17:29:47Z) - Remote Sensing Image Classification using Transfer Learning and
Attention Based Deep Neural Network [59.86658316440461]
We propose a deep learning based framework for RSISC, which makes use of the transfer learning technique and multihead attention scheme.
The proposed deep learning framework is evaluated on the benchmark NWPU-RESISC45 dataset and achieves the best classification accuracy of 94.7%.
arXiv Detail & Related papers (2022-06-20T10:05:38Z) - 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) - Deep Image Deblurring: A Survey [165.32391279761006]
Deblurring is a classic problem in low-level computer vision, which aims to recover a sharp image from a blurred input image.
Recent advances in deep learning have led to significant progress in solving this problem.
arXiv Detail & Related papers (2022-01-26T01:31:30Z) - WEDGE: Web-Image Assisted Domain Generalization for Semantic
Segmentation [72.88657378658549]
We propose a WEb-image assisted Domain GEneralization scheme, which is the first to exploit the diversity of web-crawled images for generalizable semantic segmentation.
We also present a method which injects styles of the web-crawled images into training images on-the-fly during training, which enables the network to experience images of diverse styles with reliable labels for effective training.
arXiv Detail & Related papers (2021-09-29T05:19:58Z) - Exploiting the relationship between visual and textual features in
social networks for image classification with zero-shot deep learning [0.0]
In this work, we propose a classifier ensemble based on the transferable learning capabilities of the CLIP neural network architecture.
Our experiments, based on image classification tasks according to the labels of the Places dataset, are performed by first considering only the visual part.
Considering the associated texts to the images can help to improve the accuracy depending on the goal.
arXiv Detail & Related papers (2021-07-08T10:54:59Z) - Region Comparison Network for Interpretable Few-shot Image
Classification [97.97902360117368]
Few-shot image classification has been proposed to effectively use only a limited number of labeled examples to train models for new classes.
We propose a metric learning based method named Region Comparison Network (RCN), which is able to reveal how few-shot learning works.
We also present a new way to generalize the interpretability from the level of tasks to categories.
arXiv Detail & Related papers (2020-09-08T07:29:05Z) - Generative Adversarial Networks in Digital Pathology: A Survey on Trends
and Future Potential [1.8907108368038215]
We focus on a powerful class of architectures, called Generative Adversarial Networks (GANs), applied to histological image data.
GANs enable application scenarios in this field, which were previously intractable.
We present the main applications of GANs and give an outlook of some chosen promising approaches and their possible future applications.
arXiv Detail & Related papers (2020-04-30T16:38:06Z) - 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.