Leveraging semantic segmentation for hybrid image retrieval methods
| Autor: | Michel Dhome, Marc Chevaldonné, Achref Ouni, Eric Royer |
|---|---|
| Přispěvatelé: | Institut Pascal (IP), Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Computer science Image classification InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Context (language use) 02 engineering and technology Machine learning computer.software_genre Field (computer science) 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Segmentation Image retrieval CBIR Hybrid image business.industry Deep learning [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Bag-of-words model in computer vision 020201 artificial intelligence & image processing Semantic Segmentation Artificial intelligence State (computer science) business computer Software |
| Zdroj: | Neural Computing and Applications Neural Computing and Applications, 2021, ⟨10.1007/s00521-021-06087-3⟩ Neural Computing and Applications, Springer Verlag, 2021, ⟨10.1007/s00521-021-06087-3⟩ |
| ISSN: | 0941-0643 1433-3058 |
| Popis: | International audience; Content Based Image Retrieval (CBIR) is the task of finding images in a database that are the most similar to the input query based on its visual characteristics. Several methods from the state of the art based on visual methods (Bag of visual words, VLAD, ...) or recent deep leaning methods try to solve the CBIR problem. In particular, Deep learning is a new field and used for several vision applications including CBIR. But, even with the increase of the performance of deep learning algorithms, this problem is still a challenge in computer vision. In this work, we propose three different methodologies combining deep learning based semantic segmentation and visual features. We show experimentally that by exploiting semantic information in the CBIR context leads to an increase in the retrieval accuracy. We study the performance of the proposed approach on eight different datasets (Wang, Corel-10k, Corel-5k, GHIM-10K, MSRC V1, MSRC V2, Linnaeus, NUS-WIDE) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink