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Hybrid Decomposition Convolution Neural Network and Vocabulary Forest for Image Retrieval


This paper introduces a highly efficient image retrieval technique called DCNN-vForest (Decomposition Convolution Neural Network and vocabulary Forest), which aims to retrieve the relevant images to the given image query by studying the correlation between images in the image database based on decomposition. The regional and global features of the image database are first extracted using the convolution neural network, and then divided into clusters of similar images using the Kmeans algorithm. We propose a new structure called vForest (vocabulary Forest), by calculating the vocabulary tree on each cluster of images. The retrieval process benefits from the knowledge provided by the vForest, and instead of considering the whole image database, only the most similar cluster to the image query is explored. To demonstrate the usefulness of our approach, intensive experiments have been carried out on ground-truth image databases, the results reveal the superiority of DCNN-vForest against the baseline image retrieval solutions, in terms of runtime and accuracy.


Popular scientific lecture


  • Research Council of Norway (RCN) / 296709




  • Youcef Djenouri
  • Jon Mikkelsen Hjelmervik


  • SINTEF Digital / Mathematics and Cybernetics

Presented at

International Conference on Pattern Recognition


10.01.2021 - 15.01.2021



View this publication at Cristin