Approximate Nearest Neighbor Search by Residual Vector Quantization [PDF]
Sensors, 2010 A recently proposed product quantization method is efficient for large scale approximate nearest neighbor search, however, its performance on unstructured vectors is limited.
Cheng Wang, Tao Guan, Yongjian Chen
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Multi-PQTable for Approximate Nearest-Neighbor Search [PDF]
Information, 2019 Image retrieval or content-based image retrieval (CBIR) can be transformed into the calculation of the distance between image feature vectors. The closer the vectors are, the higher the image similarity will be.
Xinpan Yuan +4 more
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Competitive Quantization for Approximate Nearest Neighbor Search [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2016 In this study, we propose a novel vector quantization algorithm for Approximate Nearest Neighbor (ANN) search, based on a joint competitive learning strategy and hence called as competitive quantization (CompQ). CompQ is a hierarchical algorithm, which iteratively minimizes the quantization error by jointly optimizing the codebooks in each layer, using
Ezgi Can Ozan +2 more
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Accumulative Quantization for Approximate Nearest Neighbor Search. [PDF]
Comput Intell Neurosci, 2022 To further improve the approximate nearest neighbor (ANN) search performance, an accumulative quantization (AQ) is proposed and applied to effective ANN search. It approximates a vector with the accumulation of several centroids, each of which is selected from a different codebook.
Ai L +6 more
europepmc +4 more sources
Capacity-Limited Failure in Approximate Nearest Neighbor Search on Image Embedding Spaces [PDF]
Journal of ImagingSimilarity search on image embeddings is a common practice for image retrieval in machine learning and pattern recognition systems. Approximate nearest neighbor (ANN) methods enable scalable similarity search on large datasets, often approaching sub ...
Morgan Roy Cooper, Mike Busch
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ON-NSW: Accelerating High-Dimensional Vector Search on Edge Devices with GPU-Optimized NSW [PDF]
SensorsThe Industrial Internet of Things (IIoT) increasingly relies on vector embeddings for analytics and AI-driven applications such as anomaly detection, predictive maintenance, and sensor fusion.
Taeyoon Park +3 more
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Binary Hashing for Approximate Nearest Neighbor Search on Big Data: A Survey
IEEE Access, 2018 Nearest neighbor search is a fundamental problem in various domains, such as computer vision, data mining, and machine learning. With the explosive growth of data on the Internet, many new data structures using spatial partitions and recursive hyperplane
Yuan Cao +6 more
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Approximate Nearest Neighbor Search Based on Neighbor Graphs with Parameter Adaptation [PDF]
Jisuanji gongcheng, 2022 Approximate Nearest Neighbor Search(ANNS) algorithms based on neighbor graphs typically organize vectors in a database into a neighbor graph structure and obtain the Approximate Nearest Neighbor(ANN) of the query vector by leveraging user-specified ...
GAN Hongnan, ZHANG Kai
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Leveraging a foundation model zoo for cell similarity search in oncological microscopy across devices [PDF]
Frontiers in OncologyBackgroundCellular imaging analysis using the traditional retrospective approach is extremely time-consuming and labor-intensive. Although AI-based solutions are available, these approaches rely heavily on supervised learning techniques that require high
Gabriel Kalweit +32 more
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Secure Cloud-Aided Approximate Nearest Neighbor Search on High-Dimensional Data
IEEE Access, 2023 As one fundamental data-mining problem, ANN (approximate nearest neighbor search) is widely used in many industries including computer vision, information retrieval, and recommendation system.
Jia Liu +7 more
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