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A Hybrid Approach for Improved Image Similarity Using Semantic Segmentation

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1
Achref Ouni
Eric Royer
Michel Dhome

Abstract

Content Based Image Retrieval(CBIR) is the task of finding the images from the datasets that consider 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. To tackle this problem, we present in this paper an efficient CBIR framework based on incorporation between deep learning based semantic segmentation and visual features. We show experimentally that the incorporate leads to the increase of accuracy of our CBIR framework. We study the performance of the proposed approach on four different datasets(Wang, MSRC V1,MSRC V2, Linnaeus)
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Dates and versions

hal-03024941 , version 1 (26-11-2020)

Licence

Attribution - CC BY 4.0

Identifiers

  • HAL Id : hal-03024941 , version 1

Cite

Achref Ouni, Eric Royer, Marc Chevaldonné, Michel Dhome. A Hybrid Approach for Improved Image Similarity Using Semantic Segmentation. ISVC, Oct 2020, virtual, United States. ⟨hal-03024941⟩
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