Image content analysis is an important surround perception modality of intelligent vehicles. In order to efficiently recognize the on-road environment based on image content analysis from the large-scale scene database, relevant images retrieval becomes one of the fundamental problems. To improve the efficiency of calculating similarities between images, hashing techniques have received increasing attentions. For most existing hash methods, the suboptimal binary codes are generated, as the hand-crafted feature representation is not optimally compatible with the binary codes. In this paper, a one-stage supervised deep hashing framework (SDHP) is proposed to learn high-quality binary codes. A deep convolutional neural network is implemented, and we enforce the learned codes to meet the following criterions: 1) similar images should be encoded into similar binary codes, and vice versa; 2) the quantization loss from Euclidean space to Hamming space should be minimized; and 3) the learned codes should be evenly distributed. The method is further extended into SDHP+ to improve the discriminative power of binary codes. Extensive experimental comparisons with state-of-the-art hashing algorithms are conducted on CIFAR-10 and NUS-WIDE, the MAP of SDHP reaches to 87.67% and 77.48% with 48 b, respectively, and the MAP of SDHP+ reaches to 91.16%, 81.08% with 12 b, 48 b on CIFAR-10 and NUS-WIDE, respectively. It illustrates that the proposed method can obviously improve the search accuracy.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Supervised Hash Coding With Deep Neural Network for Environment Perception of Intelligent Vehicles


    Beteiligte:
    Yan, Chenggang (Autor:in) / Xie, Hongtao (Autor:in) / Yang, Dongbao (Autor:in) / Yin, Jian (Autor:in) / Zhang, Yongdong (Autor:in) / Dai, Qionghai (Autor:in)


    Erscheinungsdatum :

    2018-01-01


    Format / Umfang :

    3457408 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Evidential deep learning-based multi-modal environment perception for intelligent vehicles

    Geletu, Mihreteab Negash / Giurgi, Danut-Vasile / Josso-Laurain, Thomas et al. | IEEE | 2023



    Overview of Environment Perception for Intelligent Vehicles

    Zhu, Hao / Yuen, Ka-Veng / Mihaylova, Lyudmila et al. | IEEE | 2017


    Review on Uncertainty Estimation in Deep-Learning-Based Environment Perception of Intelligent Vehicles

    Yin, Huilin / Chen, Zhaoru / Yan, Jun et al. | British Library Conference Proceedings | 2022


    Review on Uncertainty Estimation in Deep-Learning-Based Environment Perception of Intelligent Vehicles

    Rigoll, Gerhard / Yin, Huilin / Yan, Jun et al. | SAE Technical Papers | 2022