Web-Scale Image Clustering Revisited
2015 IEEE International Conference on Computer Vision (ICCV)(2015)
摘要
Large scale duplicate detection, clustering and mining of documents or images has been conventionally treated with seed detection via hashing, followed by seed growing heuristics using fast search. Principled clustering methods, especially kernelized and spectral ones, have higher complexity and are difficult to scale above millions. Under the assumption of documents or images embedded in Euclidean space, we revisit recent advances in approximate k-means variants, and borrow their best ingredients to introduce a new one, inverted-quantized k-means (IQ-means). Key underlying concepts are quantization of data points and multi-index based inverted search from centroids to cells. Its quantization is a form of hashing and analogous to seed detection, while its updates are analogous to seed growing, yet principled in the sense of distortion minimization. We further design a dynamic variant that is able to determine the number of clusters k in a single run at nearly zero additional cost. Combined with powerful deep learned representations, we achieve clustering of a 100 million image collection on a single machine in less than one hour.
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关键词
IQ-means,data point quantization,multiindex based inverted search,distortion minimization,dynamic variant design,inverted-quantized k-means,approximate k-means variants,principled clustering methods,seed growing heuristics,hashing,seed detection,document mining,document clustering,duplicate detection,Web-scale image clustering
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