Results 261 to 270 of about 8,004 (293)
Orthonormal product quantization network for scalable face image retrieval
Pattern Recognition, 2023 Existing deep quantization methods provided an efficient solution for large-scale image retrieval. However, the significant intra-class variations like pose, illumination, and expressions in face images, still pose a challenge for face image retrieval ...
Xuefei Zhe, Ming Zhang
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Optimized Product Quantization for Approximate Nearest Neighbor Search
, 2013 Product quantization is an effective vector quantization approach to compactly encode high-dimensional vectors for fast approximate nearest neighbor (ANN) search.
Kaiming He, Qifa Ke
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Online Optimized Product Quantization
2020 IEEE International Conference on Data Mining (ICDM), 2020Recently, approximate nearest neighbor(ANN) search has achieved great success in quantization models due to its high search performance, strong expression ability, and small memory space. However, most existing quantization methods are batch-based models, such as product quantization and optimized product quantization, they are not suitable for ...
Chong Liu, Defu Lian, Min Nie, Hu Xia
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Kernelized product quantization
Neurocomputing, 2017There has been increasing interest in learning compact binary codes for large-scale image data representation and retrieval. In most existing hashing-based methods, high-dimensional vectors are hashed into Hamming space, and the similarity between two vectors is approximated by the Hamming distance between their binary codes.
Jie Liu 0022 +4 more
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Learnable product quantization for anomaly detection
NeurocomputingShi Zhang, Huixia Lai
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Distribution Sensitive Product Quantization
IEEE Transactions on Circuits and Systems for Video Technology, 2018Product quantization (PQ) seems to have become the most efficient framework of performing approximate nearest neighbor (ANN) search for high-dimensional data. However, almost all existing PQ-based ANN techniques uniformly allocate precious bit budget to each subspace.
Linhao Li +3 more
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Improved embedding product quantization
Machine Vision and Applications, 2019Real-time object matching and recognition is a challenging task in computer vision probably due to the extensively computational overload posed by large and high dimensional data space. Indexing approaches can help achieving thousands of times in speedups when comparing to sequential search.
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Quantization based on a novel sample-adaptive product quantizer (SAPQ)
IEEE Transactions on Information Theory, 1999Summary: We propose a novel feedforward adaptive quantization scheme called the sample-adaptive product quantizer (SAPQ). This is a structurally constrained vector quantizer that uses unions of product codebooks. SAPQ is based on a concept of adaptive quantization to the varying samples of the source and is very different from traditional adaptation ...
Dong Sik Kim, Ness B. Shroff
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Fuzzy Norm-Explicit Product Quantization for Recommender Systems [PDF]
IEEE Transactions on Fuzzy SystemsAs the data resources grow, providing recommendations that best meet the demands has become a vital requirement in business and life to overcome the information overload problem.
Mohammadreza Jamalifard +2 more
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A Biresolution Spectral Framework for Product Quantization
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018Product quantization (PQ) (and its variants) has been effectively used to encode high-dimensional data into compact codes for many problems in vision. In principle, PQ decomposes the given data into a number of lower-dimensional subspaces where the quantization proceeds independently for each subspace. While the original PQ approach does not explicitly
Lopamudra Mukherjee +3 more
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