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
Citations
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Journal ArticleDOI
DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval
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Proceedings ArticleDOI
Online self-organizing hashing
Junxuan Chen,Yaoyi Li,Hongtao Lu +2 more
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Neighborhood kinship preserving hashing for supervised learning
TL;DR: A new neighbor kinship preserving hashing based on a learned robust distance metric is developed, which can pull the intra-class neighborhood samples as close as possible and push the inter-class Neighborhood samples as far as possible, such that the discriminant information of the training data is incorporated into the learning framework.
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Multiview Inherent Graph Hashing for Large-Scale Remote Sensing Image Retrieval
TL;DR: Wang et al. as mentioned in this paper proposed a novel multiview inherent graph hashing (MvIGH) for remote sensing image retrieval, which captures the latent similarities among RS images, and adaptively learns weights of each view to characterize its contribution.
Journal ArticleDOI
Unsupervised Balanced Hash Codes Learning With Multichannel Feature Fusion
TL;DR: Wang et al. as mentioned in this paper developed an unsupervised hashing algorithm, namely, Variational Autoencoder Balanced Hashing (VABH), to leverage multichannel feature fusion and multiscale context information to perform RSI retrieval.
References
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Dissertation
Learning Multiple Layers of Features from Tiny Images
TL;DR: In this paper, the authors describe how to train a multi-layer generative model of natural images, using a dataset of millions of tiny colour images, described in the next section.
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LIBLINEAR: A Library for Large Linear Classification
TL;DR: LIBLINEAR is an open source library for large-scale linear classification that supports logistic regression and linear support vector machines and provides easy-to-use command-line tools and library calls for users and developers.
Journal ArticleDOI
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
Aude Oliva,Antonio Torralba +1 more
TL;DR: The performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category.