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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Metric learning via feature weighting for scalable image retrieval
Xiaoming Lv,Fajie Duan +1 more
TL;DR: This work proposes a novel metric learning method, namely Metric Learning via Feature Weighting (MLFW), to effectively fuse different features and shows that the proposed MLFW outperforms the state-of-the-art methods in terms of search quality.
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Deep Binary Representation for Efficient Image Retrieval
TL;DR: Experiments on standard image retrieval benchmarks show that the proposed deep hashing method outperforms other state-of-the-art methods including unsupervised, supervised, and deep hashing methods.
Proceedings ArticleDOI
Feature Pyramid Hashing
TL;DR: A novel two-pyramid hashing architecture is proposed to learn both the semantic information and the subtle appearance details for fine-grained image search, inspired by the feature pyramids of convolutional neural network.
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Manifold-ranking embedded order preserving hashing for image semantic retrieval
TL;DR: A novel unsupervised hashing approach, namely Manifold-Ranking Embedded Order Preserving Hashing (MREOPH), which introduces a manifold ranking loss and an order preserving loss to solve the issue of global topological structure preserving.
Proceedings Article
Robust iterative quantization for efficient l p -norm similarity search
TL;DR: An ITQ+ algorithm is proposed, aiming to enhance both robustness and generalization of the original ITQ algorithm, and a lp,q-norm loss function is proposed to conduct the lp-norm similarity search, rather than a l2 norm search.
References
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Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
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Distinctive Image Features from Scale-Invariant Keypoints
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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.