We introduce SUBIC, a supervised structured binary code (concatenation of one-hot blocks). It is produced by a novel supervised deep convolutional network and is well adapted to efficient visual search, including category retrieval for which it outperforms the state-of-the-art supervised deep binary hashing techniques. For large-scale visual search, highly compressed yet meaningful representations of images are essential. Structured vector quantizers based on product quantization and its variants are usually employed to achieve such compression while minimizing the loss of accuracy. Yet, unlike binary hashing schemes, these unsupervised methods have not yet benefited from the supervision, end-to-end learning and novel architectures ushered in by the deep learning revolution. We hence propose herein a novel method to make deep convolutional neural networks produce supervised, compact, structured binary codes for visual search. Our method makes use of a novel block-softmax nonlinearity and of batch-based entropy losses that together induce structure in the learned encodings. We show that our method outperforms state-of-the-art compact representations based on deep hashing or structured quantization in single and cross-domain category retrieval, instance retrieval and classification. We make our code and models publicly available online.
A Supervised, Structured Binary Code (SUBIC) REF , used a block-softmax nonlinear function and computed batchbased entropy error to embed the structure into a binary form.
SuBiC: A Supervised, Structured Binary Code for Image Search
2017 IEEE International Conference on Computer Vision (ICCV)
2017 IEEE International Conference on Computer Vision (ICCV)
Computer Science