Journal ArticleDOI
Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval
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TLDR
This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections by proposing a simple and efficient alternating minimization algorithm, dubbed iterative quantization (ITQ), and demonstrating an application of ITQ to learning binary attributes or "classemes" on the ImageNet data set.Abstract:
This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections. We formulate this problem in terms of finding a rotation of zero-centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube, and propose a simple and efficient alternating minimization algorithm to accomplish this task. This algorithm, dubbed iterative quantization (ITQ), has connections to multiclass spectral clustering and to the orthogonal Procrustes problem, and it can be used both with unsupervised data embeddings such as PCA and supervised embeddings such as canonical correlation analysis (CCA). The resulting binary codes significantly outperform several other state-of-the-art methods. We also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to PCA or CCA. Finally, we demonstrate an application of ITQ to learning binary attributes or "classemes" on the ImageNet data set.read more
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References
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Proceedings Article
Locality-sensitive binary codes from shift-invariant kernels
Maxim Raginsky,Svetlana Lazebnik +1 more
TL;DR: This paper introduces a simple distribution-free encoding scheme based on random projections, such that the expected Hamming distance between the binary codes of two vectors is related to the value of a shift-invariant kernel between the vectors.
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Book ChapterDOI
Building Rome on a cloudless day
Jan-Michael Frahm,Pierre Fite-Georgel,David Gallup,Timothy A. Johnson,Rahul Raguram,Changchang Wu,Yi-Hung Jen,Enrique Dunn,Brian Clipp,Svetlana Lazebnik,Marc Pollefeys +10 more
TL;DR: This paper introduces an approach for dense 3D reconstruction from unregistered Internet-scale photo collections with about 3 million images within the span of a day on a single PC ("cloudless"), leveraging geometric and appearance constraints to obtain a highly parallel implementation on modern graphics processors and multi-core architectures.
Proceedings ArticleDOI
Efficient additive kernels via explicit feature maps
Andrea Vedaldi,Andrew Zisserman +1 more
TL;DR: It is shown that the χ2 kernel, which has been found to yield the best performance in most applications, also has the most compact feature representation, and is able to obtain a significant performance improvement over current state of the art results based on the intersection kernel.