145 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
Optimized Cartesian -Means
Product quantization-based approaches are effective to encode
high-dimensional data points for approximate nearest neighbor search. The space
is decomposed into a Cartesian product of low-dimensional subspaces, each of
which generates a sub codebook. Data points are encoded as compact binary codes
using these sub codebooks, and the distance between two data points can be
approximated efficiently from their codes by the precomputed lookup tables.
Traditionally, to encode a subvector of a data point in a subspace, only one
sub codeword in the corresponding sub codebook is selected, which may impose
strict restrictions on the search accuracy. In this paper, we propose a novel
approach, named Optimized Cartesian -Means (OCKM), to better encode the data
points for more accurate approximate nearest neighbor search. In OCKM, multiple
sub codewords are used to encode the subvector of a data point in a subspace.
Each sub codeword stems from different sub codebooks in each subspace, which
are optimally generated with regards to the minimization of the distortion
errors. The high-dimensional data point is then encoded as the concatenation of
the indices of multiple sub codewords from all the subspaces. This can provide
more flexibility and lower distortion errors than traditional methods.
Experimental results on the standard real-life datasets demonstrate the
superiority over state-of-the-art approaches for approximate nearest neighbor
search.Comment: to appear in IEEE TKDE, accepted in Apr. 201
Energy Based Split Vector Quantizer Employing Signal Representation in Multiple Transform Domains.
This invention relates to representation of one and multidimensional signal vectors in nonorgothonal domains and design of Vector Quantizers that can be chosen among these representations. There is presented a Vector Quantization technique in multiple nonorthogonal domains for both waveform and model based signal characterization. An iterative codebook accuracy enhancement algorithm, applicable to both waveform and model based Vector Quantization in multiple nonorthogonal domains, which yields further improvement in signal coding performance, is disclosed. Further, Vector Quantization in in nonorthogonal domains is applied to speech and exhibits clear performance improvements of reconstruction quality for the same bit rate compared to existing single domain Vector Quantization techniques. The technique disclosed herein can be easily extended to several other one and multidimensional signal classes
A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge
A vector database is used to store high-dimensional data that cannot be
characterized by traditional DBMS. Although there are not many articles
describing existing or introducing new vector database architectures, the
approximate nearest neighbor search problem behind vector databases has been
studied for a long time, and considerable related algorithmic articles can be
found in the literature. This article attempts to comprehensively review
relevant algorithms to provide a general understanding of this booming research
area. The basis of our framework categorises these studies by the approach of
solving ANNS problem, respectively hash-based, tree-based, graph-based and
quantization-based approaches. Then we present an overview of existing
challenges for vector databases. Lastly, we sketch how vector databases can be
combined with large language models and provide new possibilities
APVQ encoder applied to wideband speech coding
The paper describes a coding scheme for broadband speech (sampling frequency 16 KHz). The authors present a wideband speech encoder called APVQ (adaptive predictive vector quantization). It combines subband coding, vector quantization and adaptive prediction. The speech signal is split into 16 subbands by means of a QMF filter bank and so every subband is 500 Hz wide. This APVQ encoder can be seen as a vectorial extension of a conventional ADPCM encoder. In this scheme, signal vector is formed with one sample of the normalized prediction error signal coming from different subbands and then it is vector quantized. The prediction error signal is normalized by its gain and normalized prediction error signal is the input of the VQ and therefore an adaptive gain-shape VQ is considered. This APVQ encoder combines the advantages of scalar prediction and those of vector quantization. They evaluate wideband speech coding in the range from 1.5 to 2 bits/sample, that leads to a coding rate from 24 to 32 kbps.Peer ReviewedPostprint (published version
Hashing for Similarity Search: A Survey
Similarity search (nearest neighbor search) is a problem of pursuing the data
items whose distances to a query item are the smallest from a large database.
Various methods have been developed to address this problem, and recently a lot
of efforts have been devoted to approximate search. In this paper, we present a
survey on one of the main solutions, hashing, which has been widely studied
since the pioneering work locality sensitive hashing. We divide the hashing
algorithms two main categories: locality sensitive hashing, which designs hash
functions without exploring the data distribution and learning to hash, which
learns hash functions according the data distribution, and review them from
various aspects, including hash function design and distance measure and search
scheme in the hash coding space
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