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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Proceedings ArticleDOI
Partial-Softmax Loss based Deep Hashing
TL;DR: Zhang et al. as mentioned in this paper proposed a Partial-Softmax Loss based Deep Hashing (PSLDH) to learn discriminative hash codes for preserving the label information of images efficiently.
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DSRPH: Deep semantic-aware ranking preserving hashing for efficient multi-label image retrieval
TL;DR: A semantic-aware similarity quantization method which can measure fine-grained semantic-level similarity beyond the category based on the cosine similarity of image captions that contain high-level semantic description is designed.
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Deep Attention-Guided Hashing
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Journal ArticleDOI
Enhanced Deep Discrete Hashing with semantic-visual similarity for image retrieval
TL;DR: A fast discrete optimization algorithm is developed, which can directly generate discrete binary codes in single step, and introduce an intermediate term before iterations to avoid the problems caused by directly the use of large semantic-visual similarity matrix, which results in a significant reduction in the computational overhead.
Book ChapterDOI
Friction from Reflectance: Deep Reflectance Codes for Predicting Physical Surface Properties from One-Shot In-Field Reflectance
TL;DR: This work introduces a framework that enables prediction of actual friction values for surfaces using one-shot reflectance measurements, and develops a novel representation for reflectance disks that capture partial BRDF measurements instantaneously.
References
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Dissertation
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LIBLINEAR: A Library for Large Linear Classification
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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.