29 research outputs found
Inductive hashing on manifolds
Learning based hashing methods have attracted considerable attention due to their ability to greatly increase the scale at which existing algorithms may operate. Most of these methods are designed to generate binary codes that preserve the Euclidean distance in the original space. Manifold learning techniques, in contrast, are better able to model the intrinsic structure embedded in the original high-dimensional data. The complexity of these models, and the problems with out-of-sample data, have previously rendered them unsuitable for application to large-scale embedding, however. In this work, we consider how to learn compact binary embeddings on their intrinsic manifolds. In order to address the above-mentioned difficulties, we describe an efficient, inductive solution to the out-of-sample data problem, and a process by which non-parametric manifold learning may be used as the basis of a hashing method. Our proposed approach thus allows the development of a range of new hashing techniques exploiting the flexibility of the wide variety of manifold learning approaches available. We particularly show that hashing on the basis of t-SNE [29] outperforms state-of-the-art hashing methods on large-scale benchmark datasets, and is very effective for image classification with very short code lengths.Fumin Shen, Chunhua Shen, Qinfeng Shi, Anton van den Hengel, Zhenmin Tanghttp://www.pamitc.org/cvpr13
A General Two-Step Approach to Learning-Based Hashing
Most existing approaches to hashing apply a single form of hash function, and
an optimization process which is typically deeply coupled to this specific
form. This tight coupling restricts the flexibility of the method to respond to
the data, and can result in complex optimization problems that are difficult to
solve. Here we propose a flexible yet simple framework that is able to
accommodate different types of loss functions and hash functions. This
framework allows a number of existing approaches to hashing to be placed in
context, and simplifies the development of new problem-specific hashing
methods. Our framework decomposes hashing learning problem into two steps: hash
bit learning and hash function learning based on the learned bits. The first
step can typically be formulated as binary quadratic problems, and the second
step can be accomplished by training standard binary classifiers. Both problems
have been extensively studied in the literature. Our extensive experiments
demonstrate that the proposed framework is effective, flexible and outperforms
the state-of-the-art.Comment: 13 pages. Appearing in Int. Conf. Computer Vision (ICCV) 201
Zero-Shot Hashing via Transferring Supervised Knowledge
Hashing has shown its efficiency and effectiveness in facilitating
large-scale multimedia applications. Supervised knowledge e.g. semantic labels
or pair-wise relationship) associated to data is capable of significantly
improving the quality of hash codes and hash functions. However, confronted
with the rapid growth of newly-emerging concepts and multimedia data on the
Web, existing supervised hashing approaches may easily suffer from the scarcity
and validity of supervised information due to the expensive cost of manual
labelling. In this paper, we propose a novel hashing scheme, termed
\emph{zero-shot hashing} (ZSH), which compresses images of "unseen" categories
to binary codes with hash functions learned from limited training data of
"seen" categories. Specifically, we project independent data labels i.e.
0/1-form label vectors) into semantic embedding space, where semantic
relationships among all the labels can be precisely characterized and thus seen
supervised knowledge can be transferred to unseen classes. Moreover, in order
to cope with the semantic shift problem, we rotate the embedded space to more
suitably align the embedded semantics with the low-level visual feature space,
thereby alleviating the influence of semantic gap. In the meantime, to exert
positive effects on learning high-quality hash functions, we further propose to
preserve local structural property and discrete nature in binary codes.
Besides, we develop an efficient alternating algorithm to solve the ZSH model.
Extensive experiments conducted on various real-life datasets show the superior
zero-shot image retrieval performance of ZSH as compared to several
state-of-the-art hashing methods.Comment: 11 page
Optimizing Ranking Measures for Compact Binary Code Learning
Hashing has proven a valuable tool for large-scale information retrieval.
Despite much success, existing hashing methods optimize over simple objectives
such as the reconstruction error or graph Laplacian related loss functions,
instead of the performance evaluation criteria of interest---multivariate
performance measures such as the AUC and NDCG. Here we present a general
framework (termed StructHash) that allows one to directly optimize multivariate
performance measures. The resulting optimization problem can involve
exponentially or infinitely many variables and constraints, which is more
challenging than standard structured output learning. To solve the StructHash
optimization problem, we use a combination of column generation and
cutting-plane techniques. We demonstrate the generality of StructHash by
applying it to ranking prediction and image retrieval, and show that it
outperforms a few state-of-the-art hashing methods.Comment: Appearing in Proc. European Conference on Computer Vision 201
Facial image restoration and retrieval through orthogonality
University of Technology Sydney. Faculty of Engineering and Information Technology.Orthogonality has different definitions in geometry, statistics and calculus. This thesis studies how to incorporate orthogonality to facial image restoration and retrieval tasks. A facial image restoration method and three retrieval methods were proposed.
Blur in facial images significantly impedes the efficiency of recognition approaches. However, most existing blind deconvolution methods cannot generate satisfactory results, due to their dependence on strong edges which are sufficient in natural images but not in facial images. A novel method is proposed in this report. Point spread functions (PSF) are represented by the linear combination of a set of pre-defined orthogonal PSFs and similarly, an estimated intrinsic sharp face image (EI) is represented by the linear combination of a set of pre-defined orthogonal face images. In doing so, PSF and EI estimation is simplified to discovering two sets of linear combination coefficients which are simultaneously found by the proposed coupled learning algorithm. To make the method robust to different kinds of blurry face images, several candidate PSFs and EIs are generated for a test image, and then a non-blind deconvolution method is adopted to generate more EIs by those candidate PSFs. Finally, a blind image quality assessment metric is deployed to automatically select the optimal EI.
On the other hand, the orthogonality is incorporated into the proposed Unimodal image retrieval method. Hashing methods have been widely investigated for fast approximate nearest neighbor searching in large datasets. Most existing methods use binary vectors in lower dimensional spaces to represent data points that are usually real vectors of higher dimensionality. The proposed method divides the hashing process into two steps. Data points are first embedded in a low-dimensional space, and the Global Positioning System (GPS) method is subsequently introduced but modified for binary embedding. Data-independent and data-dependent methods are devised to distribute the satellites at appropriate locations. The proposed methods are based on finding the tradeoff between the information losses in these two steps. Experiments show that the data-dependent method outperforms other methods in different-sized datasets from 100K to 10M. By incorporating the orthogonality of the code matrix, both data-independent and data-dependent methods are particularly impressive in experiments on longer bits.
In social networks, heterogeneous multimedia data correlates to each other, such as videos and their corresponding tags in YouTube and image-text pairs in Facebook. Nearest neighbor retrieval across multiple modalities on large data sets becomes a hot yet challenging problem. Hashing is expected to be an efficient solution, since it represents data as binary codes. As the bit-wise XOR operations can be fast handled, the retrieval time is greatly reduced. Few existing multi-modal hashing methods consider the correlation among hashing bits. The correlation has negative impact on hashing codes. When the hashing code length becomes longer, the retrieval performance improvement becomes slower. The proposed method incorporates a so-called minimum correlation constraint which can be treated as a generalization of orthogonality constraint. Experiments show the superiority of the proposed method becomes greater as the code length increases.
Deep neural network is expected to be an efficient way for multi-modal hashing. We propose a hybrid neural network which consists of a convolutional neural network for facial images and a full-connected neural network for tags or labels. The minimum correlation regularization is imposed on the parameters of output layers. Experiments validates the superiority of the proposed hybrid neural network
Fast Supervised Hashing with Decision Trees for High-Dimensional Data
Supervised hashing aims to map the original features to compact binary codes
that are able to preserve label based similarity in the Hamming space.
Non-linear hash functions have demonstrated the advantage over linear ones due
to their powerful generalization capability. In the literature, kernel
functions are typically used to achieve non-linearity in hashing, which achieve
encouraging retrieval performance at the price of slow evaluation and training
time. Here we propose to use boosted decision trees for achieving non-linearity
in hashing, which are fast to train and evaluate, hence more suitable for
hashing with high dimensional data. In our approach, we first propose
sub-modular formulations for the hashing binary code inference problem and an
efficient GraphCut based block search method for solving large-scale inference.
Then we learn hash functions by training boosted decision trees to fit the
binary codes. Experiments demonstrate that our proposed method significantly
outperforms most state-of-the-art methods in retrieval precision and training
time. Especially for high-dimensional data, our method is orders of magnitude
faster than many methods in terms of training time.Comment: Appearing in Proc. IEEE Conf. Computer Vision and Pattern
Recognition, 2014, Ohio, US