Neural codes for image retrieval

Artem Babenko, Anton Slesarev, Alexandr Chigorin, Victor Lempitsky

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    812 Citations (Scopus)


    It has been shown that the activations invoked by an image within the top layers of a large convolutional neural network provide a high-level descriptor of the visual content of the image. In this paper, we investigate the use of such descriptors (neural codes) within the image retrieval application. In the experiments with several standard retrieval benchmarks, we establish that neural codes perform competitively even when the convolutional neural network has been trained for an unrelated classification task (e.g. Image-Net). We also evaluate the improvement in the retrieval performance of neural codes, when the network is retrained on a dataset of images that are similar to images encountered at test time. We further evaluate the performance of the compressed neural codes and show that a simple PCA compression provides very good short codes that give state-of-the-art accuracy on a number of datasets. In general, neural codes turn out to be much more resilient to such compression in comparison other state-of-the-art descriptors. Finally, we show that discriminative dimensionality reduction trained on a dataset of pairs of matched photographs improves the performance of PCA-compressed neural codes even further. Overall, our quantitative experiments demonstrate the promise of neural codes as visual descriptors for image retrieval.

    Original languageEnglish
    Title of host publicationComputer Vision, ECCV 2014 - 13th European Conference, Proceedings
    PublisherSpringer Verlag
    Number of pages16
    EditionPART 1
    ISBN (Print)9783319105895
    Publication statusPublished - 2014
    Event13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland
    Duration: 6 Sep 201412 Sep 2014

    Publication series

    NameLecture Notes in Computer Science


    Conference13th European Conference on Computer Vision, ECCV 2014


    • convolutional neural networks
    • deep learning
    • feature extraction
    • image retrieval
    • same-object image search


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