@article {18242,
	title = {Modeling and Analysis of Correlated Binary Fingerprints for Content Identification},
	journal = {Information Forensics and Security, IEEE Transactions on},
	volume = {6},
	year = {2011},
	month = {2011/09//},
	pages = {1146 - 1159},
	abstract = {Multimedia identification via content fingerprints is used in many applications, such as content filtering on user-generated content websites, and automatic multimedia identification and tagging. A compact $\#$x201C;fingerprint $\#$x201D; is computed for each multimedia signal that captures robust and unique properties of the perceptual content, which is later used for identifying the multimedia. Several different multimedia fingerprinting schemes have been proposed in the literature and have been evaluated through experiments. To complement these experimental evaluations and provide guidelines for choosing system parameters and designing better schemes, this paper develops models for content fingerprinting and provides an analysis of the identification performance under these models. As a first step, bounds on the identification accuracy and the required fingerprint length for the simplest case when the fingerprint bits are modeled as i.i.d. are summarized. Markov Random Fields are then used to address more realistic settings of fingerprints with correlated components. The optimal likelihood ratio detector is derived and a statistical physics inspired approach for computing the probability of detection and probability of false alarm is described. The analysis shows that the commonly used Hamming distance detection criterion is susceptible to correlations among fingerprint bits, whereas the optimal log-likelihood ratio decision rule yields 5-20\% improvement in the accuracy over a range of correlations. Simulation results demonstrate the validity of the theoretical predictions.},
	keywords = {alarm, approach;user, binary, computing;, content, criterion;Markov, decision, DETECTION, detector;statistical, distance, fields;content, filtering;content, filtering;multimedia, fingerprinting, fingerprints;detection, generated, Hamming, identification;correlated, identification;optimal, inspired, likelihood, Physics, probability;false, probability;log, processes;Web, random, ratio, rule;multimedia, schemes;multimedia, sites;information, Websites;Markov},
	isbn = {1556-6013},
	doi = {10.1109/TIFS.2011.2152394},
	author = {Varna,A.L. and M. Wu}
}
@conference {13991,
	title = {Microphone Arrays as Generalized Cameras for Integrated Audio Visual Processing},
	booktitle = {Computer Vision and Pattern Recognition, 2007. CVPR {\textquoteright}07. IEEE Conference on},
	year = {2007},
	month = {2007/06//},
	pages = {1 - 8},
	abstract = {Combinations of microphones and cameras allow the joint audio visual sensing of a scene. Such arrangements of sensors are common in biological organisms and in applications such as meeting recording and surveillance where both modalities are necessary to provide scene understanding. Microphone arrays provide geometrical information on the source location, and allow the sound sources in the scene to be separated and the noise suppressed, while cameras allow the scene geometry and the location and motion of people and other objects to be estimated. In most previous work the fusion of the audio-visual information occurs at a relatively late stage. In contrast, we take the viewpoint that both cameras and microphone arrays are geometry sensors, and treat the microphone arrays as generalized cameras. We employ computer-vision inspired algorithms to treat the combined system of arrays and cameras. In particular, we consider the geometry introduced by a general microphone array and spherical microphone arrays. The latter show a geometry that is very close to central projection cameras, and we show how standard vision based calibration algorithms can be profitably applied to them. Experiments are presented that demonstrate the usefulness of the considered approach.},
	keywords = {algorithms;generalized, arrays;scene, arrays;sensor, audio, audio-visual, cameras;geometrical, fusion;, fusion;computer-vision, geometry;sound, information, information;geometry, inspired, location;acoustic, processing;array, processing;audio-visual, processing;microphone, sensors;integrated, signal, sources;source, systems;cameras;computer, vision;geometry;microphone, visual},
	doi = {10.1109/CVPR.2007.383345},
	author = {O{\textquoteright}Donovan,A. and Duraiswami, Ramani and Neumann, J.}
}
