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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Supervised hashing with kernels
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Semantic hashing
TL;DR: In this paper, a deep graphical model of the word-count vectors obtained from a large set of documents is proposed. But the model is restricted to the deep layer of the deep neural network and cannot handle large numbers of documents.
Proceedings ArticleDOI
Iterative quantization: A procrustean approach to learning binary codes
Yunchao Gong,Svetlana Lazebnik +1 more
TL;DR: A simple and efficient alternating minimization scheme for 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 is proposed.
Proceedings ArticleDOI
Classification using intersection kernel support vector machines is efficient
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Proceedings Article
Hashing with Graphs
TL;DR: This paper proposes a novel graph-based hashing method which automatically discovers the neighborhood structure inherent in the data to learn appropriate compact codes and describes a hierarchical threshold learning procedure in which each eigenfunction yields multiple bits, leading to higher search accuracy.