PTN-10 Supervised Hash Coding With Deep Neural Network For Environment Perception Of Intelligent Vehicles

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Abstruct: 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 NUSWIDE,
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