466 research outputs found
Unsupervised Triplet Hashing for Fast Image Retrieval
Hashing has played a pivotal role in large-scale image retrieval. With the
development of Convolutional Neural Network (CNN), hashing learning has shown
great promise. But existing methods are mostly tuned for classification, which
are not optimized for retrieval tasks, especially for instance-level retrieval.
In this study, we propose a novel hashing method for large-scale image
retrieval. Considering the difficulty in obtaining labeled datasets for image
retrieval task in large scale, we propose a novel CNN-based unsupervised
hashing method, namely Unsupervised Triplet Hashing (UTH). The unsupervised
hashing network is designed under the following three principles: 1) more
discriminative representations for image retrieval; 2) minimum quantization
loss between the original real-valued feature descriptors and the learned hash
codes; 3) maximum information entropy for the learned hash codes. Extensive
experiments on CIFAR-10, MNIST and In-shop datasets have shown that UTH
outperforms several state-of-the-art unsupervised hashing methods in terms of
retrieval accuracy
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
Similarity-preserving hashing is a widely-used method for nearest neighbour
search in large-scale image retrieval tasks. For most existing hashing methods,
an image is first encoded as a vector of hand-engineering visual features,
followed by another separate projection or quantization step that generates
binary codes. However, such visual feature vectors may not be optimally
compatible with the coding process, thus producing sub-optimal hashing codes.
In this paper, we propose a deep architecture for supervised hashing, in which
images are mapped into binary codes via carefully designed deep neural
networks. The pipeline of the proposed deep architecture consists of three
building blocks: 1) a sub-network with a stack of convolution layers to produce
the effective intermediate image features; 2) a divide-and-encode module to
divide the intermediate image features into multiple branches, each encoded
into one hash bit; and 3) a triplet ranking loss designed to characterize that
one image is more similar to the second image than to the third one. Extensive
evaluations on several benchmark image datasets show that the proposed
simultaneous feature learning and hash coding pipeline brings substantial
improvements over other state-of-the-art supervised or unsupervised hashing
methods.Comment: This paper has been accepted to IEEE International Conference on
Pattern Recognition and Computer Vision (CVPR), 201
Video retrieval based on deep convolutional neural network
Recently, with the enormous growth of online videos, fast video retrieval
research has received increasing attention. As an extension of image hashing
techniques, traditional video hashing methods mainly depend on hand-crafted
features and transform the real-valued features into binary hash codes. As
videos provide far more diverse and complex visual information than images,
extracting features from videos is much more challenging than that from images.
Therefore, high-level semantic features to represent videos are needed rather
than low-level hand-crafted methods. In this paper, a deep convolutional neural
network is proposed to extract high-level semantic features and a binary hash
function is then integrated into this framework to achieve an end-to-end
optimization. Particularly, our approach also combines triplet loss function
which preserves the relative similarity and difference of videos and
classification loss function as the optimization objective. Experiments have
been performed on two public datasets and the results demonstrate the
superiority of our proposed method compared with other state-of-the-art video
retrieval methods
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