Journal ArticleDOI
Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval
Reads0
Chats0
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
More filters
Journal ArticleDOI
Fast Binary Embeddings and Quantized Compressed Sensing with Structured Matrices
Thang Huynh,Rayan Saab +1 more
TL;DR: In this paper, the authors proposed fast quantization methods for distance-preserving binary embeddings and quantization for compressed sensing in bounded orthonormal ensembles and partial circulant matrices.
Proceedings ArticleDOI
Deep Spherical Quantization for Image Search
Sepehr Eghbali,Ladan Tahvildari +1 more
TL;DR: Deep Spherical Quantization (DSQ) is put forward, a novel method to make deep convolutional neural networks generate supervised and compact binary codes for efficient image search and an easy-to-implement extension of the quantization technique that enforces sparsity on the codebooks is introduced.
Journal ArticleDOI
In Defense of Locality-Sensitive Hashing
TL;DR: This paper developed the locality-sensitive two-step hashing (LS-TSH) that generates the binary codes through LSH rather than any complex optimization technique, and could obtain comparable retrieval accuracy with state of the arts with two to three orders of magnitudes faster training speed.
Proceedings ArticleDOI
Supervised Discrete Hashing With Mutual Linear Regression
TL;DR: A novel learning-based hashing method termed supervised discrete hashing with mutual linear regression (SDHMLR) is proposed in this study, where only one stable projection is used to describe the linear correlation between hash codes and corresponding labels.
Journal ArticleDOI
Incremental Hashing for Semantic Image Retrieval in Nonstationary Environments
TL;DR: The incremental hashing (ICH) method is proposed, which uses a multihashing to retain knowledge coming from images arriving over time and a weight-based ranking to make the retrieval results adaptive to the new data environment.
References
More filters
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.