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, etc.) 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 in 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 and NUS-WIDE)
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