1,553 research outputs found
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
Covariance Eigenvector Sparsity for Compression and Denoising
Sparsity in the eigenvectors of signal covariance matrices is exploited in
this paper for compression and denoising. Dimensionality reduction (DR) and
quantization modules present in many practical compression schemes such as
transform codecs, are designed to capitalize on this form of sparsity and
achieve improved reconstruction performance compared to existing
sparsity-agnostic codecs. Using training data that may be noisy a novel
sparsity-aware linear DR scheme is developed to fully exploit sparsity in the
covariance eigenvectors and form noise-resilient estimates of the principal
covariance eigenbasis. Sparsity is effected via norm-one regularization, and
the associated minimization problems are solved using computationally efficient
coordinate descent iterations. The resulting eigenspace estimator is shown
capable of identifying a subset of the unknown support of the eigenspace basis
vectors even when the observation noise covariance matrix is unknown, as long
as the noise power is sufficiently low. It is proved that the sparsity-aware
estimator is asymptotically normal, and the probability to correctly identify
the signal subspace basis support approaches one, as the number of training
data grows large. Simulations using synthetic data and images, corroborate that
the proposed algorithms achieve improved reconstruction quality relative to
alternatives.Comment: IEEE Transcations on Signal Processing, 2012 (to appear
Efficient end-to-end learning for quantizable representations
Embedding representation learning via neural networks is at the core
foundation of modern similarity based search. While much effort has been put in
developing algorithms for learning binary hamming code representations for
search efficiency, this still requires a linear scan of the entire dataset per
each query and trades off the search accuracy through binarization. To this
end, we consider the problem of directly learning a quantizable embedding
representation and the sparse binary hash code end-to-end which can be used to
construct an efficient hash table not only providing significant search
reduction in the number of data but also achieving the state of the art search
accuracy outperforming previous state of the art deep metric learning methods.
We also show that finding the optimal sparse binary hash code in a mini-batch
can be computed exactly in polynomial time by solving a minimum cost flow
problem. Our results on Cifar-100 and on ImageNet datasets show the state of
the art search accuracy in precision@k and NMI metrics while providing up to
98X and 478X search speedup respectively over exhaustive linear search. The
source code is available at
https://github.com/maestrojeong/Deep-Hash-Table-ICML18Comment: Accepted and to appear at ICML 2018. Camera ready versio
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