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
Weakly Supervised Multimodal Hashing for Scalable Social Image Retrieval
Jinhui Tang,Zechao Li +1 more
TL;DR: Extensive experiments are conducted on two widely used social image data sets and the encouraging performance compared with the state-of-the-art hashing techniques demonstrates the effectiveness of the proposed method.
Journal ArticleDOI
Part-based Deep Hashing for Large-scale Person Re-identification
TL;DR: Part-based deep hashing (PDH) as mentioned in this paper integrates spatial information for discriminative visual representation by partitioning the pedestrian image into horizontal parts, in which batches of triplet samples are employed as the input of the deep hashing architecture, each triplet sample contains two pedestrian images (or parts) with the same identity and one pedestrian image (or part) of the different identity.
Proceedings ArticleDOI
Auto-Encoding Twin-Bottleneck Hashing
TL;DR: This paper proposes an efficient and adaptive code-driven graph, which is updated by decoding in the context of an auto-encoder, and introduces into the framework twin bottlenecks that exchange crucial information collaboratively.
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Fracking Deep Convolutional Image Descriptors.
TL;DR: A siamese architecture of Deep Convolutional Neural Networks, with a Hinge embedding loss on the L2 distance between descriptors is explored, with large performance gains compared to both standard CNN learning strategies, hand-crafted image descriptors, and the state-of-the-art on learned descriptors.
Proceedings ArticleDOI
Discrete Deep Learning for Fast Content-Aware Recommendation
TL;DR: This paper proposes a hashing based deep learning framework called Discrete Deep Learning (DDL), to map users and items to Hamming space, where a user»s preference for an item can be efficiently calculated by Hamming distance, and this computation scheme significantly improves the efficiency of online recommendation.
References
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Dissertation
Learning Multiple Layers of Features from Tiny Images
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Journal Article
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.