A Survey of Hand Crafted and Deep Learning Methods for Image Aesthetic Assessment

• URL: http://arxiv.org/abs/2103.11616v1
• Date: Mon, 22 Mar 2021 07:00:56 GMT
• Title: A Survey of Hand Crafted and Deep Learning Methods for Image Aesthetic Assessment
• Authors: Saira Kanwal, Muhammad Uzair, Habib Ullah
• Abstract summary: This paper presents a literature review of the recent techniques of automatic image aesthetics assessment. A large number of traditional hand crafted and deep learning based approaches are reviewed.
• Score: 2.9005223064604078
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic image aesthetics assessment is a computer vision problem that deals with the categorization of images into different aesthetic levels. The categorization is usually done by analyzing an input image and computing some measure of the degree to which the image adhere to the key principles of photography (balance, rhythm, harmony, contrast, unity, look, feel, tone and texture). Owing to its diverse applications in many areas, automatic image aesthetic assessment has gained significant research attention in recent years. This paper presents a literature review of the recent techniques of automatic image aesthetics assessment. A large number of traditional hand crafted and deep learning based approaches are reviewed. Key problem aspects are discussed such as why some features or models perform better than others and what are the limitations. A comparison of the quantitative results of different methods is also provided at the end.

Related papers

• Structuring Quantitative Image Analysis with Object Prominence [0.0]
  We suggest carefully considering objects' prominence as an essential step in analyzing images as data. Our approach combines qualitative analyses with the scalability of quantitative approaches.
  arXiv  Detail & Related papers  (2024-08-30T19:05:28Z)
• Image Aesthetics Assessment via Learnable Queries [59.313054821874864]
  We propose the Image Aesthetics Assessment via Learnable Queries (IAA-LQ) approach. It adapts learnable queries to extract aesthetic features from pre-trained image features obtained from a frozen image encoder. Experiments on real-world data demonstrate the advantages of IAA-LQ, beating the best state-of-the-art method by 2.2% and 2.1% in terms of SRCC and PLCC, respectively.
  arXiv  Detail & Related papers  (2023-09-06T09:42:16Z)
• Learning to Evaluate the Artness of AI-generated Images [64.48229009396186]
  ArtScore is a metric designed to evaluate the degree to which an image resembles authentic artworks by artists. We employ pre-trained models for photo and artwork generation, resulting in a series of mixed models. This dataset is then employed to train a neural network that learns to estimate quantized artness levels of arbitrary images.
  arXiv  Detail & Related papers  (2023-05-08T17:58:27Z)
• A survey on facial image deblurring [3.6775758132528877]
  When the facial image is blurred, it has a great impact on high-level vision tasks such as face recognition. This paper surveys and summarizes recently published methods for facial image deblurring, most of which are based on deep learning. We show the performance of classical methods on datasets and metrics and give a brief discussion on the differences of model-based and learning-based methods.
  arXiv  Detail & Related papers  (2023-02-10T02:24:56Z)
• A domain adaptive deep learning solution for scanpath prediction of paintings [66.46953851227454]
  This paper focuses on the eye-movement analysis of viewers during the visual experience of a certain number of paintings. We introduce a new approach to predicting human visual attention, which impacts several cognitive functions for humans. The proposed new architecture ingests images and returns scanpaths, a sequence of points featuring a high likelihood of catching viewers' attention.
  arXiv  Detail & Related papers  (2022-09-22T22:27:08Z)
• Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment [6.201485014848172]
  We propose the Critique Photo Reddit dataset (RPCD), which contains 74K images and 220K comments. We exploit the polarity of the sentiment of criticism as an indicator of aesthetic judgment.
  arXiv  Detail & Related papers  (2022-06-17T08:16:20Z)
• Recent Advances in Scene Image Representation and Classification [1.8369974607582584]
  We review the existing scene image representation methods that are being used widely for image classification. We compare their performance both qualitatively (e.g., quality of outputs, pros/cons, etc.) and quantitatively (e.g., accuracy) Overall, this survey provides in-depth insights and applications of recent scene image representation methods for traditional Computer Vision (CV)-based methods, Deep Learning (DL)-based methods, and Search Engine (SE)-based methods.
  arXiv  Detail & Related papers  (2022-06-15T07:12:23Z)
• 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)
• Image Quality Assessment in the Modern Age [53.19271326110551]
  This tutorial provides the audience with the basic theories, methodologies, and current progresses of image quality assessment (IQA) We will first revisit several subjective quality assessment methodologies, with emphasis on how to properly select visual stimuli. Both hand-engineered and (deep) learning-based methods will be covered.
  arXiv  Detail & Related papers  (2021-10-19T02:38:46Z)
• Automatic Main Character Recognition for Photographic Studies [78.88882860340797]
  Main characters in images are the most important humans that catch the viewer's attention upon first look. Identifying the main character in images plays an important role in traditional photographic studies and media analysis. We propose a method for identifying the main characters using machine learning based human pose estimation.
  arXiv  Detail & Related papers  (2021-06-16T18:14:45Z)
• Deep Metric Learning for Few-Shot Image Classification: A Selective Review [38.71276383292809]
  Few-shot image classification is a challenging problem which aims to achieve the human level of recognition based only on a small number of images. Deep learning algorithms such as meta-learning, transfer learning, and metric learning have been employed recently and achieved the state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-05-17T20:27:59Z)

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.