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
Compact Hash Code Learning With Binary Deep Neural Network
TL;DR: Zhang et al. as discussed by the authors constrain one hidden layer to directly output the binary codes and incorporate independence and balance properties in the direct and strict forms into the learning schemes, and they also include a similarity preserving property in their objective functions.
Journal ArticleDOI
Robust Cross-view Hashing for Multimedia Retrieval
TL;DR: A novel cross-view hashing method, where a common Hamming space is learned such that binary codes from different views are consistent and comparable, is proposed, and the results demonstrate the superiority of RCH over many other state-of-the-art methods in terms of retrieval performance and cross-View consistency.
Proceedings ArticleDOI
Adversarial Factorization Autoencoder for Look-alike Modeling
TL;DR: A novel Adversarial Factorization Autoencoder is proposed that can efficiently learn a binary mapping from sparse, high-dimensional data to a binary address space through the use of an adversarial training procedure.
Journal ArticleDOI
Collective Affinity Learning for Partial Cross-Modal Hashing
TL;DR: A novel Collective Affinity Learning Method (CALM), which collectively and adaptively learns an anchor graph for generating binary codes on partial multi-modal data and demonstrates its ability to recover missing adjacency information.
Journal ArticleDOI
Dual-Level Semantic Transfer Deep Hashing for Efficient Social Image Retrieval
TL;DR: Wang et al. as discussed by the authors proposed a dual-level semantic transfer deep hashing (DSTDH) method to solve the semantic shortage problem in unsupervised deep hashing, which does not require any manually labeled data for training.
References
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ImageNet: A large-scale hierarchical image database
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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.