Museum Painting Retrieval

• URL: http://arxiv.org/abs/2105.04891v1
• Date: Tue, 11 May 2021 09:28:14 GMT
• Title: Museum Painting Retrieval
• Authors: \`Oscar Lorente, Ian Riera, Shauryadeep Chaudhuri, Oriol Catalan, V\'ictor Casales
• Abstract summary: We implement a query by example retrieval system for finding paintings in a museum image collection using classic computer vision techniques. We study the performance of the color, texture, text and feature descriptors in datasets with different perturbations in the images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To retrieve images based on their content is one of the most studied topics in the field of computer vision. Nowadays, this problem can be addressed using modern techniques such as feature extraction using machine learning, but over the years different classical methods have been developed. In this paper, we implement a query by example retrieval system for finding paintings in a museum image collection using classic computer vision techniques. Specifically, we study the performance of the color, texture, text and feature descriptors in datasets with different perturbations in the images: noise, overlapping text boxes, color corruption and rotation. We evaluate each of the cases using the Mean Average Precision (MAP) metric, and we obtain results that vary between 0.5 and 1.0 depending on the problem conditions.

Related papers

• Visual Motif Identification: Elaboration of a Curated Comparative Dataset and Classification Methods [4.431754853927668]
  In cinema, visual motifs are recurrent iconographic compositions that carry artistic or aesthetic significance. Our goal is to recognise and classify these motifs by proposing a new machine learning model that uses a custom dataset to that end. We show how features extracted from a CLIP model can be leveraged by using a shallow network and an appropriate loss to classify images into 20 different motifs, with surprisingly good results.
  arXiv  Detail & Related papers  (2024-10-21T10:50:00Z)
• Blind Dates: Examining the Expression of Temporality in Historical Photographs [57.07335632641355]
  We investigate the dating of images using OpenCLIP, an open-source implementation of CLIP, a multi-modal language and vision model. We use the textitDe Boer Scene Detection dataset, containing 39,866 gray-scale historical press photographs from 1950 to 1999. Our analysis reveals that images featuring buses, cars, cats, dogs, and people are more accurately dated, suggesting the presence of temporal markers.
  arXiv  Detail & Related papers  (2023-10-10T13:51:24Z)
• Deep Image Matting: A Comprehensive Survey [85.77905619102802]
  This paper presents a review of recent advancements in image matting in the era of deep learning. We focus on two fundamental sub-tasks: auxiliary input-based image matting and automatic image matting. We discuss relevant applications of image matting and highlight existing challenges and potential opportunities for future research.
  arXiv  Detail & Related papers  (2023-04-10T15:48:55Z)
• Leveraging Computer Vision Application in Visual Arts: A Case Study on the Use of Residual Neural Network to Classify and Analyze Baroque Paintings [0.0]
  In this case study, we focus on the classification of a selected painting 'Portrait of the Painter Charles Bruni' by Johann Kupetzky. We show that the features extracted during residual network training can be useful for image retrieval within search systems in online art collections.
  arXiv  Detail & Related papers  (2022-10-27T10:15:36Z)
• Pattern Spotting and Image Retrieval in Historical Documents using Deep Hashing [60.67014034968582]
  This paper presents a deep learning approach for image retrieval and pattern spotting in digital collections of historical documents. Deep learning models are used for feature extraction, considering two distinct variants, which provide either real-valued or binary code representations. The proposed approach also reduces the search time by up to 200x and the storage cost up to 6,000x when compared to related works.
  arXiv  Detail & Related papers  (2022-08-04T01:39:37Z)
• Where Does the Performance Improvement Come From? - A Reproducibility Concern about Image-Text Retrieval [85.03655458677295]
  Image-text retrieval has gradually become a major research direction in the field of information retrieval. We first examine the related concerns and why the focus is on image-text retrieval tasks. We analyze various aspects of the reproduction of pretrained and nonpretrained retrieval models.
  arXiv  Detail & Related papers  (2022-03-08T05:01:43Z)
• Image Classification with Classic and Deep Learning Techniques [0.0]
  We implement an image classifier using both classic computer vision and deep learning techniques. We evaluate each of the cases in terms of accuracy and loss, and we obtain results that vary between 0.6 and 0.96 depending on the model and configuration used.
  arXiv  Detail & Related papers  (2021-05-11T09:32:38Z)
• A Decade Survey of Content Based Image Retrieval using Deep Learning [13.778851745408133]
  This paper presents a comprehensive survey of deep learning based developments in the past decade for content based image retrieval. The similarity between the representative features of the query image and dataset images is used to rank the images for retrieval. Deep learning has emerged as a dominating alternative of hand-designed feature engineering from a decade.
  arXiv  Detail & Related papers  (2020-11-23T02:12:30Z)
• Image Colorization: A Survey and Dataset [94.59768013860668]
  This article presents a comprehensive survey of state-of-the-art deep learning-based image colorization techniques. It categorizes the existing colorization techniques into seven classes and discusses important factors governing their performance. We perform an extensive experimental evaluation of existing image colorization methods using both existing datasets and our proposed one.
  arXiv  Detail & Related papers  (2020-08-25T01:22:52Z)
• Distilling Localization for Self-Supervised Representation Learning [82.79808902674282]
  Contrastive learning has revolutionized unsupervised representation learning. Current contrastive models are ineffective at localizing the foreground object. We propose a data-driven approach for learning in variance to backgrounds.
  arXiv  Detail & Related papers  (2020-04-14T16:29:42Z)

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.