Nearest-Neighbor Methods in Learning and Vision

By Gregory Shakhnarovich, Trevor Darrell, Piotr Indyk. 2008

330 citations recorded by the source corpus.

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Citations in the corpus

Product Quantization for Nearest Neighbor Search (2011, background): “In this paper, we consider the Euclidean distance, which is relevant for many applications.”
Approximate Nearest Neighbor: Towards Removing the Curse of Dimensionality (2012, methodology): “They have been successfully applied to computational problems in a variety of areas, including web clustering [HGI00], computational biology [Buh01, BT01], computer vision (see selected articles in [SDI06]), computational drug design [DGJC06] and computational linguistics [RPH05].”
Aggregating local descriptors into a compact image representation (2010, methodology): “Approximate nearest neighbors search methods [4, 11, 15, 24, 27] are required to handle large databases in computer vision applications [22].”
Searching with quantization: approximate nearest neighbor search using short codes and distance estimators (2009, methodology): “In the case of E2LSH, the memory usage may even be higher than that of the original vectors, i.e., several hundreds of bytes.”
Binary Generative Adversarial Networks for Image Retrieval (2017, background): “With the rapidly increasing amount of images, similarity search in large image collections has been actively pursued in a number of domains, including computer vision, information retrieval and pattern recognition [39, 43].”
Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions (2006, neutral): “, [7, 23, 21, 25, 16, 24, 17, 12, 8, 28, 1], see also [31]).”
Large Scale Nearest Neighbors Search Based on Neighborhood Graph (2013, background): “Finding the k-nearest neighbors (KNN) of a single query point in an extremely large collection of high-dimensional vectors is a fundamental problem in multimedia retrieval such as image retrieval [1, 2, 3], computer vision [4], music retrieval [5], etc.”
Inner Product Similarity Search using Compositional Codes (2014, background): “It has attracted a lot of interests [2] in computer vision and pattern recognition because of the popularity of large scale and highdimensional multimedia data.”
Searching with expectations (2010, methodology): “It has been successfully used for local descriptors [7] and 3D object indexing [12, 9].”
Fast approximate similarity search based on degree-reduced neighborhood graphs (2011, methodology): “…search index, the proposed method utilizes a degree-reduced k-nearest neighbor(k-DR )graph constructed from the object set with the dissimilarity, and explores the k-DR graph along its edges using a greedy search(GS ) algorithm starting from multiple initial vertices with parallel processing.”