A Hybrid Approach for Improved Image Similarity Using Semantic Segmentation

Ouni, Achref; Royer, Eric; Chevaldonné, Marc; Dhome, Michel

doi:10.1007/978-3-030-64559-5_51

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“…We show that the use of semantic information offers potential for improvement over standard approaches with benefits in terms of accuracy and computation time. It is an extension of the framework we initially proposed in [79], with a new image representation proposal and a complete study of the framework through extensive experiments. Three methodologies are proposed to build the image signature as well as a semantic filter to obtain our final image representation.…”

Section: Introductionmentioning

confidence: 99%

Leveraging semantic segmentation for hybrid image retrieval methods

Ouni

Royer

Chevaldonné

et al. 2021

Neural Comput & Applic

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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)

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Section: Introductionmentioning

confidence: 99%