1,448 research outputs found
Online Product Quantization
Approximate nearest neighbor (ANN) search has achieved great success in many
tasks. However, existing popular methods for ANN search, such as hashing and
quantization methods, are designed for static databases only. They cannot
handle well the database with data distribution evolving dynamically, due to
the high computational effort for retraining the model based on the new
database. In this paper, we address the problem by developing an online product
quantization (online PQ) model and incrementally updating the quantization
codebook that accommodates to the incoming streaming data. Moreover, to further
alleviate the issue of large scale computation for the online PQ update, we
design two budget constraints for the model to update partial PQ codebook
instead of all. We derive a loss bound which guarantees the performance of our
online PQ model. Furthermore, we develop an online PQ model over a sliding
window with both data insertion and deletion supported, to reflect the
real-time behaviour of the data. The experiments demonstrate that our online PQ
model is both time-efficient and effective for ANN search in dynamic large
scale databases compared with baseline methods and the idea of partial PQ
codebook update further reduces the update cost.Comment: To appear in IEEE Transactions on Knowledge and Data Engineering
(DOI: 10.1109/TKDE.2018.2817526
Scalable Image Retrieval by Sparse Product Quantization
Fast Approximate Nearest Neighbor (ANN) search technique for high-dimensional
feature indexing and retrieval is the crux of large-scale image retrieval. A
recent promising technique is Product Quantization, which attempts to index
high-dimensional image features by decomposing the feature space into a
Cartesian product of low dimensional subspaces and quantizing each of them
separately. Despite the promising results reported, their quantization approach
follows the typical hard assignment of traditional quantization methods, which
may result in large quantization errors and thus inferior search performance.
Unlike the existing approaches, in this paper, we propose a novel approach
called Sparse Product Quantization (SPQ) to encoding the high-dimensional
feature vectors into sparse representation. We optimize the sparse
representations of the feature vectors by minimizing their quantization errors,
making the resulting representation is essentially close to the original data
in practice. Experiments show that the proposed SPQ technique is not only able
to compress data, but also an effective encoding technique. We obtain
state-of-the-art results for ANN search on four public image datasets and the
promising results of content-based image retrieval further validate the
efficacy of our proposed method.Comment: 12 page
Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval
In this paper, we propose a novel deep generative approach to cross-modal
retrieval to learn hash functions in the absence of paired training samples
through the cycle consistency loss. Our proposed approach employs adversarial
training scheme to lean a couple of hash functions enabling translation between
modalities while assuming the underlying semantic relationship. To induce the
hash codes with semantics to the input-output pair, cycle consistency loss is
further proposed upon the adversarial training to strengthen the correlations
between inputs and corresponding outputs. Our approach is generative to learn
hash functions such that the learned hash codes can maximally correlate each
input-output correspondence, meanwhile can also regenerate the inputs so as to
minimize the information loss. The learning to hash embedding is thus performed
to jointly optimize the parameters of the hash functions across modalities as
well as the associated generative models. Extensive experiments on a variety of
large-scale cross-modal data sets demonstrate that our proposed method achieves
better retrieval results than the state-of-the-arts.Comment: To appeared on IEEE Trans. Image Processing. arXiv admin note: text
overlap with arXiv:1703.10593 by other author
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