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 Decade Survey of Content Based Image Retrieval using Deep Learning
Dana Sukau,Shiv Ram Dubey +1 more
TL;DR: This paper presents a comprehensive survey of deep learning based developments in the past decade for content based image retrieval, covering different supervision, different networks, different descriptor type and different retrieval type.
Journal ArticleDOI
Supervised Hashing Using Graph Cuts and Boosted Decision Trees
TL;DR: The proposed framework allows a number of existing approaches to hashing to be placed in context, and simplifies the development of new problem-specific hashing methods, and decomposes the hashing learning problem into two steps: binary code (hash bit) learning and hash function learning.
Journal ArticleDOI
Learning Visual Semantic Relationships for Efficient Visual Retrieval
TL;DR: This paper investigates how to establish the relationship between semantic concepts based on the large-scale realworld click data from image commercial engine, which is a challenging topic because the click data suffers from the noise such as typos.
Proceedings ArticleDOI
Online Multi-modal Hashing with Dynamic Query-adaption
TL;DR: A self-weighted fusion strategy is designed to adaptively preserve the multi-modal feature information into hash codes by exploiting their complementarity, while avoiding the challenging symmetric similarity matrix factorization.
Posted Content
Zero-Shot Sketch-Image Hashing
TL;DR: ZSIH is the first zero- shot hashing work suitable for SBIR and cross-modal search and forms a generative hashing scheme in reconstructing semantic knowledge representations for zero-shot retrieval.
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.