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In this work, we present "HDIdx", an efficient high-dimensional indexing library for fast approximate NN search, which is open-source and written in Python. ... Fast Nearest Neighbor (NN) search is a fundamental challenge in large-scale data processing and analytics, particularly for analyzing multimedia contents which are often of high dimensionality. ... Conclusions This work presented the HDIdx software that offers cutting-edge solutions for indexing largescale high-dimensional data. ...doi:10.1016/j.neucom.2015.11.104 fatcat:h5gsn6m2yfbphfb4mxiapceoba
In order to increase the search speed through data warehouses for knowledge discovery, new indexing methods are required to handle both the size and the informational complexity of EO data. ... In this paper, we propose a methodology that combines feature extraction, hashing methods and optimized indexing to convert the images characteristics into hash codes in an effort to speed up the search ... A product quantization method for approximate nearest neighbor search is presented in  . Combined with an inverted file system provides high efficiency. ...doi:10.1109/igarss.2019.8898911 dblp:conf/igarss/GriveiVD19 fatcat:4kxa6lfhp5huvordgnbsfowgme
As the particular challenges of mobile visual search, achieving high recognition bitrate becomes the consistent target of existed related works. ... In this paper, we explore to holistically exploit the deep learning-based hashing methods for more robust and instant mobile visual search. ... Deep learning hashing 1.3.1 Background Hashing, a widely-studied solution to approximate nearest neighbor search, aims to transform the data item to a low-dimensional representation, or equivalently a ...doi:10.1186/s13640-017-0167-4 fatcat:vcdhjjbe6jai7hyigxstihcega
Therefore, we developed an intelligent fashion technique based on deep learning for efficient fashion product searches and recommendations consisting of a Sketch-Product fashion retrieval model and vector-based ... Therefore, a system that efficiently supports the searching and recommendation of a product is becoming increasingly important. ... Acknowledgments: The authors thank the MSIT, IITP, and NRF for supporting the research and project. Conflicts of Interest: The authors declare no conflicts of interest. ...doi:10.3390/electronics9030508 fatcat:kmffxze2zbemfhqwxcioksveui