A New CBIR Model Using Semantic Segmentation and Fast Spatial Binary Encoding.

Content Based Image Retrieval (CBIR) is the task of finding similar images from a query one. Since the term similar means here "with the same semantic content", we propose to explore in this paper, a framework that uses Deep Neural Networks based semantic segmentation networks, coupled with a binary spatial encoding. Such simple representation has several relevant properties: 1) It takes advantage of the state of the art semantic segmentation networks and 2) the proposed binary encoding allows a Hamming distance that requests a very low computation budget resulting to a fast CBIR method. Several experiments achieved on public datasets show that our binary semantic signature leads to increase the CBIR accuracy and reduce the execution time. We study the performance of the proposed approach on six different public datasets: Wang, Corel 10k, GHIM-10K, MSRC-V1, MSRC-V2, Linnaeus.