917 research outputs found
Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval
Free-hand sketch-based image retrieval (SBIR) is a specific cross-view
retrieval task, in which queries are abstract and ambiguous sketches while the
retrieval database is formed with natural images. Work in this area mainly
focuses on extracting representative and shared features for sketches and
natural images. However, these can neither cope well with the geometric
distortion between sketches and images nor be feasible for large-scale SBIR due
to the heavy continuous-valued distance computation. In this paper, we speed up
SBIR by introducing a novel binary coding method, named \textbf{Deep Sketch
Hashing} (DSH), where a semi-heterogeneous deep architecture is proposed and
incorporated into an end-to-end binary coding framework. Specifically, three
convolutional neural networks are utilized to encode free-hand sketches,
natural images and, especially, the auxiliary sketch-tokens which are adopted
as bridges to mitigate the sketch-image geometric distortion. The learned DSH
codes can effectively capture the cross-view similarities as well as the
intrinsic semantic correlations between different categories. To the best of
our knowledge, DSH is the first hashing work specifically designed for
category-level SBIR with an end-to-end deep architecture. The proposed DSH is
comprehensively evaluated on two large-scale datasets of TU-Berlin Extension
and Sketchy, and the experiments consistently show DSH's superior SBIR
accuracies over several state-of-the-art methods, while achieving significantly
reduced retrieval time and memory footprint.Comment: This paper will appear as a spotlight paper in CVPR201
Unsupervised Generative Adversarial Cross-modal Hashing
Cross-modal hashing aims to map heterogeneous multimedia data into a common
Hamming space, which can realize fast and flexible retrieval across different
modalities. Unsupervised cross-modal hashing is more flexible and applicable
than supervised methods, since no intensive labeling work is involved. However,
existing unsupervised methods learn hashing functions by preserving inter and
intra correlations, while ignoring the underlying manifold structure across
different modalities, which is extremely helpful to capture meaningful nearest
neighbors of different modalities for cross-modal retrieval. To address the
above problem, in this paper we propose an Unsupervised Generative Adversarial
Cross-modal Hashing approach (UGACH), which makes full use of GAN's ability for
unsupervised representation learning to exploit the underlying manifold
structure of cross-modal data. The main contributions can be summarized as
follows: (1) We propose a generative adversarial network to model cross-modal
hashing in an unsupervised fashion. In the proposed UGACH, given a data of one
modality, the generative model tries to fit the distribution over the manifold
structure, and select informative data of another modality to challenge the
discriminative model. The discriminative model learns to distinguish the
generated data and the true positive data sampled from correlation graph to
achieve better retrieval accuracy. These two models are trained in an
adversarial way to improve each other and promote hashing function learning.
(2) We propose a correlation graph based approach to capture the underlying
manifold structure across different modalities, so that data of different
modalities but within the same manifold can have smaller Hamming distance and
promote retrieval accuracy. Extensive experiments compared with 6
state-of-the-art methods verify the effectiveness of our proposed approach.Comment: 8 pages, accepted by 32th AAAI Conference on Artificial Intelligence
(AAAI), 201
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