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
A Performance Evaluation of Hashing Techniques for 2D and 3D Palmprint Retrieval and Recognition
TL;DR: Performance evaluation of hashing techniques for 2D and 3D palmprint retrieval and recognition shows that some hashing methods such as column sampling based discrete supervised hashing (COSDISH) can obtain promising results.
Journal ArticleDOI
An ELM based local topology preserving hashing
TL;DR: An ELM based local topology preserving hashing (ELMLTPH) method is proposed to realize efficient hashing learning for large scale applications and with the facilitation of ELM, original data topology is effectively preserved to hamming space.
Proceedings ArticleDOI
Searching Privately by Imperceptible Lying: A Novel Private Hashing Method with Differential Privacy
Yimu Wang,Shiyin Lu,Lijun Zhang +2 more
TL;DR: This paper proposes a novel noise mechanism, Random Flipping, and two private hashing algorithms, i.e., PHashing and PITQ, with the refined analysis within the framework of differential privacy, since differential privacy is a well-established technique to measure the privacy leakage of an algorithm.
Journal ArticleDOI
Fast hard negative mining for deep metric learning
TL;DR: Bag of Negatives (BoN) is an efficient method that selects a bag of hard negatives based on a novel online hashing strategy that shows the superiority of BoN against state-of-the-art hard negative mining methods in terms of accuracy and training time over three large datasets.
Journal ArticleDOI
Deep learned compact binary descriptor with a lightweight network-in-network architecture for visual description
TL;DR: This work proposes a new learning approach using a lightweight DNN architecture via a stack of multiple multilayer perceptrons based on the network in network (NIN) architecture, and a restricted Boltzmann machine (RBM) that produces the learned binary descriptor that outperforms other baseline self-supervised binary descriptors in terms of correspondence matching despite the smaller size of its DNN.
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.