611 research outputs found
Privacy-Preserving Identification via Layered Sparse Code Design: Distributed Servers and Multiple Access Authorization
We propose a new computationally efficient privacy-preserving identification
framework based on layered sparse coding. The key idea of the proposed
framework is a sparsifying transform learning with ambiguization, which
consists of a trained linear map, a component-wise nonlinearity and a privacy
amplification. We introduce a practical identification framework, which
consists of two phases: public and private identification. The public untrusted
server provides the fast search service based on the sparse privacy protected
codebook stored at its side. The private trusted server or the local client
application performs the refined accurate similarity search using the results
of the public search and the layered sparse codebooks stored at its side. The
private search is performed in the decoded domain and also the accuracy of
private search is chosen based on the authorization level of the client. The
efficiency of the proposed method is in computational complexity of encoding,
decoding, "encryption" (ambiguization) and "decryption" (purification) as well
as storage complexity of the codebooks.Comment: EUSIPCO 201
Learning to compress and search visual data in large-scale systems
The problem of high-dimensional and large-scale representation of visual data
is addressed from an unsupervised learning perspective. The emphasis is put on
discrete representations, where the description length can be measured in bits
and hence the model capacity can be controlled. The algorithmic infrastructure
is developed based on the synthesis and analysis prior models whose
rate-distortion properties, as well as capacity vs. sample complexity
trade-offs are carefully optimized. These models are then extended to
multi-layers, namely the RRQ and the ML-STC frameworks, where the latter is
further evolved as a powerful deep neural network architecture with fast and
sample-efficient training and discrete representations. For the developed
algorithms, three important applications are developed. First, the problem of
large-scale similarity search in retrieval systems is addressed, where a
double-stage solution is proposed leading to faster query times and shorter
database storage. Second, the problem of learned image compression is targeted,
where the proposed models can capture more redundancies from the training
images than the conventional compression codecs. Finally, the proposed
algorithms are used to solve ill-posed inverse problems. In particular, the
problems of image denoising and compressive sensing are addressed with
promising results.Comment: PhD thesis dissertatio
Local Higher-Order Statistics (LHS) describing images with statistics of local non-binarized pixel patterns
Accepted for publication in International Journal of Computer Vision and Image Understanding (CVIU)International audienceWe propose a new image representation for texture categorization and facial analysis, relying on the use of higher-order local differential statistics as features. It has been recently shown that small local pixel pattern distributions can be highly discriminative while being extremely efficient to compute, which is in contrast to the models based on the global structure of images. Motivated by such works, we propose to use higher-order statistics of local non-binarized pixel patterns for the image description. The proposed model does not require either (i) user specified quantization of the space (of pixel patterns) or (ii) any heuristics for discarding low occupancy volumes of the space. We propose to use a data driven soft quantization of the space, with parametric mixture models, combined with higher-order statistics, based on Fisher scores. We demonstrate that this leads to a more expressive representation which, when combined with discriminatively learned classifiers and metrics, achieves state-of-the-art performance on challenging texture and facial analysis datasets, in low complexity setup. Further, it is complementary to higher complexity features and when combined with them improves performance
Incremental dimension reduction of tensors with random index
We present an incremental, scalable and efficient dimension reduction
technique for tensors that is based on sparse random linear coding. Data is
stored in a compactified representation with fixed size, which makes memory
requirements low and predictable. Component encoding and decoding are performed
on-line without computationally expensive re-analysis of the data set. The
range of tensor indices can be extended dynamically without modifying the
component representation. This idea originates from a mathematical model of
semantic memory and a method known as random indexing in natural language
processing. We generalize the random-indexing algorithm to tensors and present
signal-to-noise-ratio simulations for representations of vectors and matrices.
We present also a mathematical analysis of the approximate orthogonality of
high-dimensional ternary vectors, which is a property that underpins this and
other similar random-coding approaches to dimension reduction. To further
demonstrate the properties of random indexing we present results of a synonym
identification task. The method presented here has some similarities with
random projection and Tucker decomposition, but it performs well at high
dimensionality only (n>10^3). Random indexing is useful for a range of complex
practical problems, e.g., in natural language processing, data mining, pattern
recognition, event detection, graph searching and search engines. Prototype
software is provided. It supports encoding and decoding of tensors of order >=
1 in a unified framework, i.e., vectors, matrices and higher order tensors.Comment: 36 pages, 9 figure
Online hashing for fast similarity search
In this thesis, the problem of online adaptive hashing for fast similarity search is studied. Similarity search is a central problem in many computer vision applications. The ever-growing size of available data collections and the increasing usage of high-dimensional representations in describing data have increased the computational cost of performing similarity search, requiring search strategies that can explore such collections in an efficient and effective manner. One promising family of approaches is based on hashing, in which the goal is to map the data into the Hamming space where fast search mechanisms exist, while preserving the original neighborhood structure of the data. We first present a novel online hashing algorithm in which the hash mapping is updated in an iterative manner with streaming data. Being online, our method is amenable to variations of the data. Moreover, our formulation is orders of magnitude faster to train than state-of-the-art hashing solutions. Secondly, we propose an online supervised hashing framework in which the goal is to map data associated with similar labels to nearby binary representations. For this purpose, we utilize Error Correcting Output Codes (ECOCs) and consider an online boosting formulation in learning the hash mapping. Our formulation does not require any prior assumptions on the label space and is well-suited for expanding datasets that have new label inclusions. We also introduce a flexible framework that allows us to reduce hash table entry updates. This is critical, especially when frequent updates may occur as the hash table grows larger and larger. Thirdly, we propose a novel mutual information measure to efficiently infer the quality of a hash mapping and retrieval performance. This measure has lower complexity than standard retrieval metrics. With this measure, we first address a key challenge in online hashing that has often been ignored: the binary representations of the data must be recomputed to keep pace with updates to the hash mapping. Based on our novel mutual information measure, we propose an efficient quality measure for hash functions, and use it to determine when to update the hash table. Next, we show that this mutual information criterion can be used as an objective in learning hash functions, using gradient-based optimization. Experiments on image retrieval benchmarks confirm the effectiveness of our formulation, both in reducing hash table recomputations and in learning high-quality hash functions
- …