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
Discrete Multi-graph Hashing for Large-Scale Visual Search
TL;DR: A novel hashing method, called discrete multi-graph hashing (DMGH), which uses a multi- graph learning technique to fuse multiple views, and adaptively learns the weights of each view to reduce the distortion error in the quantization stage.
Posted Content
DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs.
TL;DR: Zhang et al. as mentioned in this paper investigated the relationship between the initial noisy similarity signals learned from local structures and the semantic similarity labels assigned by a Bayes optimal classifier, and designed a simple yet effective strategy to distill data pairs automatically and further adopt a Bayesian learning framework to learn hash functions from the distilled data set.
Proceedings ArticleDOI
Exploring Deep Multimodal Fusion of Text and Photo for Hate Speech Classification
TL;DR: It is shown that augmenting text with image embedding information immediately leads to a boost in performance, while applying additional attention fusion methods brings further improvement.
Journal ArticleDOI
Two-Stream Deep Hashing With Class-Specific Centers for Supervised Image Search
TL;DR: This work designs a neural network that leverages label information and outputs a unified binary representation for each class and designs an image network to learn hash codes from images and force these hash codes to be close to the corresponding class-specific centers.
Book ChapterDOI
Product Quantization Network for Fast Image Retrieval
TL;DR: Through the proposed product quantization network, the author can obtain a discriminative and compact image representation in an end-to-end manner, which further enables a fast and accurate image retrieval.
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.