Compact Binary Fingerprint for Image Copy Re-Ranking

• URL: http://arxiv.org/abs/2109.07802v1
• Date: Thu, 16 Sep 2021 08:44:56 GMT
• Title: Compact Binary Fingerprint for Image Copy Re-Ranking
• Authors: Nazar Mohammad, Junaid Baber, Maheen Bakhtyar, Bilal Ahmed Chandio, Anwar Ali Sanjrani
• Abstract summary: Image copy detection is challenging and appealing topic in computer vision and signal processing. Local keypoint descriptors such as SIFT are used to represent the images, and based on those descriptors matching, images are matched and retrieved. Features are quantized so that searching/matching may be made feasible for large databases at the cost of accuracy loss.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image copy detection is challenging and appealing topic in computer vision and signal processing. Recent advancements in multimedia have made distribution of image across the global easy and fast: that leads to many other issues such as forgery and image copy retrieval. Local keypoint descriptors such as SIFT are used to represent the images, and based on those descriptors matching, images are matched and retrieved. Features are quantized so that searching/matching may be made feasible for large databases at the cost of accuracy loss. In this paper, we propose binary feature that is obtained by quantizing the SIFT into binary, and rank list is re-examined to remove the false positives. Experiments on challenging dataset shows the gain in accuracy and time.

Related papers

• Active Image Indexing [26.33727468288776]
  This paper improves the robustness of image copy detection with active indexing. We reduce the quantization loss of a given image representation by making imperceptible changes to the image before its release. Experiments show that the retrieval and copy detection of activated images is significantly improved.
  arXiv  Detail & Related papers  (2022-10-05T17:55:15Z)
• Document Image Binarization in JPEG Compressed Domain using Dual Discriminator Generative Adversarial Networks [0.0]
  The proposed model has been thoroughly tested with different versions of DIBCO dataset having challenges like holes, erased or smudged ink, dust, and misplaced fibres. The model proved to be highly robust, efficient both in terms of time and space complexities, and also resulted in state-of-the-art performance in JPEG compressed domain.
  arXiv  Detail & Related papers  (2022-09-13T12:07:32Z)
• SImProv: Scalable Image Provenance Framework for Robust Content Attribution [80.25476792081403]
  We present SImProv, a framework to match a query image back to a trusted database of originals. SimProv consists of three stages: a scalable search stage for retrieving top-k most similar images; a re-ranking and near-duplicated detection stage for identifying the original among the candidates. We demonstrate effective retrieval and manipulation detection over a dataset of 100 million images.
  arXiv  Detail & Related papers  (2022-06-28T18:42:36Z)
• Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images [60.89777029184023]
  We propose a semi-supervised CD model in which we formulate an unsupervised CD loss in addition to the supervised Cross-Entropy (CE) loss. Experiments conducted on two publicly available CD datasets show that the proposed semi-supervised CD method can reach closer to the performance of supervised CD.
  arXiv  Detail & Related papers  (2022-04-18T17:59:01Z)
• A Self-Supervised Descriptor for Image Copy Detection [13.624995441674642]
  We introduce SSCD, a model that builds on a self-supervised contrastive training objective. We adapt this method to the copy detection task by changing the architecture and training objective. Our approach relies on an entropy regularization term, promoting consistent separation between descriptor vectors.
  arXiv  Detail & Related papers  (2022-02-21T14:25:32Z)
• The 2021 Image Similarity Dataset and Challenge [32.202821997745716]
  This paper introduces a new benchmark for large-scale image similarity detection. The goal is to determine whether a query image is a modified copy of any image in a reference corpus of size 1million.
  arXiv  Detail & Related papers  (2021-06-17T17:23:59Z)
• Compositional Sketch Search [91.84489055347585]
  We present an algorithm for searching image collections using free-hand sketches. We exploit drawings as a concise and intuitive representation for specifying entire scene compositions.
  arXiv  Detail & Related papers  (2021-06-15T09:38:09Z)
• Benchmarking Scientific Image Forgery Detectors [18.225190509954874]
  This paper presents an extendable open-source library that reproduces the most common image forgery operations reported by the research integrity community. We create a large scientific forgery image benchmark (39,423 images) with an enriched ground-truth. In addition, concerned about the high number of retracted papers due to image duplication, this work evaluates the state-of-the-art copy-move detection methods in the proposed dataset.
  arXiv  Detail & Related papers  (2021-05-26T22:58:20Z)
• Modeling Lost Information in Lossy Image Compression [72.69327382643549]
  Lossy image compression is one of the most commonly used operators for digital images. We propose a novel invertible framework called Invertible Lossy Compression (ILC) to largely mitigate the information loss problem.
  arXiv  Detail & Related papers  (2020-06-22T04:04:56Z)
• Compact Deep Aggregation for Set Retrieval [87.52470995031997]
  We focus on retrieving images containing multiple faces from a large scale dataset of images. Here the set consists of the face descriptors in each image, and given a query for multiple identities, the goal is then to retrieve, in order, images which contain all the identities. We show that this compact descriptor has minimal loss of discriminability up to two faces per image, and degrades slowly after that.
  arXiv  Detail & Related papers  (2020-03-26T08:43:15Z)
• Discernible Image Compression [124.08063151879173]
  This paper aims to produce compressed images by pursuing both appearance and perceptual consistency. Based on the encoder-decoder framework, we propose using a pre-trained CNN to extract features of the original and compressed images. Experiments on benchmarks demonstrate that images compressed by using the proposed method can also be well recognized by subsequent visual recognition and detection models.
  arXiv  Detail & Related papers  (2020-02-17T07:35:08Z)

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.