Slava Voloshynovskiy

Fast content identification in high-dimensional feature spaces using Sparse Ternary Codes

We consider the problem of fast content identification in high-dimensional feature spaces where a sub-linear search complexity is required. By formulating the problem as sparse approximation of projected coefficients, a closed-form solution can be found which we approximate as a ternary representation. Hence, as opposed to dense binary codes, a framework of Sparse Ternary Codes (STC) is proposed resulting in sparse, but robust representation and sub-linear complexity of search. The proposed method is compared with the Locality Sensitive Hashing (LSH) and the memory vectors on several large-scale synthetic and public image databases, showing its superiority.

Sparse ternary codes for similarity search have higher coding gain than dense binary codes

This paper addresses the problem of Approximate Nearest Neighbor (ANN) search in pattern recognition where feature vectors in a database are encoded as compact codes in order to speed-up the similarity search in large-scale databases. Considering the ANN problem from an information-theoretic perspective, we interpret it as an encoding, which maps the original feature vectors to a less entropic sparse representation while requiring them to be as informative as possible. We then define the coding gain for ANN search using information-theoretic measures. We next show that the classical approach to this problem, which consists of binarization of the projected vectors is sub-optimal. Instead, a properly designed ternary encoding achieves higher coding gains and lower complexity.

Local active content fingerprinting: Optimal solution under linear modulation

This papers presents an analysis on Active Content Fingerprint (aCFP) for local (patch based) image descriptors. A generalization is proposed, the reduction of the aCFP with linear modulation to a constrained projection problem is shown and the optimal solution is given. The constrained projection problem addresses the linear modulation by a constraint on the properties of the resulting local descriptor. A computer simulation using local image patches, extracted from publicly available data sets is provided, demonstrating the advantages under several signal processing distortions.

Physical object authentication: Detection-theoretic comparison of natural and artificial randomness

In this paper, we compare two methods that can be used by the anti-counterfeiting industry to protect physical objects, which are either based on an objects natural randomness or on artificial randomness embedded on the object. We show that the considered verification architectures rely either on a comparison between an enrolled fingerprint and an extracted one or between a tag and a fingerprint. We compare these setups from detection-theoretic perspectives for both types of architectures. Authentication performance using false and miss error probabilities of the two systems are analysed and then compared using two practical setups. We highlight the advantages and limitations of each architecture. These theoretical results derived for binary fingerprints are useful to construct and optimise practical methods and to help select the appropriate architecture.

Joint learning of local fingerprint and content modulation

This paper proposes learning a linear map with local content modulation for robust content fingerprinting. The goal is to estimate a data adapted linear map that provides bounded modulation distortion and features with targeted properties. A novel problem formulation is presented that jointly addresses the fingerprint learning and the content modulation. A solution by iterative alternating algorithm is proposed. The algorithm alternates between liner map update step and linear modulation estimate step. Global optimal solutions for the respective iterative steps are proposed, resulting in convergent algorithm with locally optimal solution. A computer simulation using local image patches, extracted from publicly available data set is provided. The advantages under additive white Gaussian noise (AWGN), lossy JPEG compression and projective geometrical transform distortions are demonstrated.

Local Active Content Fingerprint: Solutions for general linear feature maps

This paper presents solutions to the local patch based Active Content Fingerprint (aCFP) with linear modulation, general linear feature map and convex constraints on the properties of the local feature descriptor. A direct approximation of the linear feature map such that the image distortion is as small as possible and the approximate linear feature map is as close as possible to the original map is proposed. Then an explicit regularization of the trade-off between the modulation distortion and the robustness of the local feature is introduced trough a novel problem formulation. A computer simulation using local image patches, extracted from publicly available data set is provided, demonstrating the advantages under: additive white Gaussian noise (AWGN), lossy JPEG compression and projective geometrical transform distortions.

Soft Content Fingerprinting With Bit Polarization Based on Sign-Magnitude Decomposition

Content identification based on digital content fingerprinting attracts significant attention in different emerging applications. In this paper, we consider content identification based on the sign-magnitude decomposition of fingerprint codewords and analyze the achievable rates for sign and magnitude components. We demonstrate that the bit robustness in the sign channel, often used in binary fingerprinting, is determined by the value of the corresponding magnitude component. Correspondingly, one can distinguish between two systems depending how the information about the magnitude component is used at the decoding process, i.e., hard fingerprinting when this information is disregarded, and soft fingerprinting when this information is used. To reveal the advantages of soft information at the decoding, we consider a case of soft fingerprinting where the decoder has access to the complete information about the uncoded magnitude component. However, since it requires a lot of extra memory storage or secure communication, the magnitude information is often quantized to a single bit or extracted directly from the noisy observation. To generalize the existing methods and estimate the impact of quantization and noise in the side information about the magnitude components on the achievable rate, we introduce a channel splitting approach and reveal certain interesting phenomena related to channel polarization. We demonstrate that under proper quantization of the magnitude component, one can clearly observe the existence of strong components whose sign is very robust, even to strong distortions. We demonstrate that under certain conditions, a great portion of the rate in the sign channel is concentrated in strong channel components. Finally, we demonstrate how to use the channel splitting property in the design of efficient low-complexity identification methods.