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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Book ChapterDOI
Modeling Winner-Take-All Competition in Sparse Binary Projections
TL;DR: In this paper, a supervised-WTA model is proposed for sparse binary projection models, which works when training samples with both input and output representations are available, from which the optimal projection matrix can be obtained with a simple, efficient, yet effective algorithm after proper relaxation.
Journal ArticleDOI
Fast discrete cross-modal hashing with semantic consistency.
TL;DR: A Fast Discrete Cross-modal Hashing method that firstly leverages both class labels and the pair-wise similarity matrix to learn a sharing Hamming space where the semantic consistency can be better preserved, and proposes an asymmetric hash codes learning model to avoid the challenging issue of symmetric matrix factorization.
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Unsupervised Concatenation Hashing with Sparse Constraint for Cross-Modal Retrieval.
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Central Similarity Hashing via Hadamard matrix.
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Journal ArticleDOI
Deep learning compact binary codes for fingerprint indexing
TL;DR: A novel fingerprint indexing method based on deep neural networks to learn DCBMCC, which has an extremely small error rate with a very low penetration rate and incorporates independence, balance, quantization-loss-minimum, and similarity-preservation properties in this learning process.
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.
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
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.
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.