2018/12/07 by Yosr Ghozzi, Nesrine Baklouti, Ghozzi, Yosr +7
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Fuzzy Logic and Control Systems #Image Retrieval and Classification Techniques #Multi-Criteria Decision Making #cs.AI #cs.CV
paper · pdf · doi:10.48550/arxiv.1812.07098
10 pages, 7 figures, 1 table
arxiv created 2018/12/07 · openalex publication_date 2018/12/07 · arxiv updated 2018/12/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In an automated search system, similarity is a key concept in solving a human task. Indeed, human process is usually a natural categorization that underlies many natural abilities such as image recovery, language comprehension, decision making, or pattern recognition. In the image search axis, there are several ways to measure the similarity between images in an image database, to a query image. Image search by content is based on the similarity of the visual characteristics of the images. The distance function used to evaluate the similarity between images depends on the criteria of the search but also on the representation of the characteristics of the image; this is the main idea of the near and fuzzy sets approaches. In this article, we introduce a new category of beta type-2 fuzzy sets for the description of image characteristics as well as the near sets approach for image recovery. Finally, we illustrate our work with examples of image recovery problems used in the real world.