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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...
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...
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...
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...
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...
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 object's 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...
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,...
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