We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

We present a new algorithm for manifold learning and nonlinear dimensionality reduction. Based on a set of unorganized data points sampled with noise from a parameterized manifold, the local geometry of the manifold is learned by constructing an approximation for the tangent space at each data point, and those tangent spaces are then aligned to give the global coordinates of the data points with respect to the underlying manifold. We also present an error analysis of our algorithm showing that reconstruction errors can be quite small in some cases. We illustrate our algorithm using curves and surfaces both in two-dimensional/three-dimensional (2D/3D) Euclidean spaces and in higher-dimensional Euclidean spaces. We also address several theoretical and algorithmic issues for further research and improvements.

/pdf/principal-manifolds-and-nonlinear-dimensionality-reduction-442mrhilga.pdf

Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment

The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …

Information retrieval

Speech emotion recognition using deep 1D & 2D CNN LSTM networks

We address the problem of tracking and recognizing faces in real-world, noisy videos. We track faces using a tracker that adaptively builds a target model reflecting changes in appearance, typical of a video setting. However, adaptive appearance trackers often suffer from drift, a gradual adaptation of the tracker to non-targets. To alleviate this problem, our tracker introduces visual constraints using a combination of generative and discriminative models in a particle filtering framework. The generative term conforms the particles to the space of generic face poses while the discriminative one ensures rejection of poorly aligned targets. This leads to a tracker that significantly improves robustness against abrupt appearance changes and occlusions, critical for the subsequent recognition phase. Identity of the tracked subject is established by fusing pose-discriminant and person-discriminant features over the duration of a video sequence. This leads to a robust video-based face recognizer with state-of-the-art recognition performance. We test the quality of tracking and face recognition on real-world noisy videos from YouTube as well as the standard Honda/UCSD database. Our approach produces successful face tracking results on over 80% of all videos without video or person-specific parameter tuning. The good tracking performance induces similarly high recognition rates: 100% on Honda/UCSD and over 70% on the YouTube set containing 35 celebrities in 1500 sequences.

/pdf/face-tracking-and-recognition-with-visual-constraints-in-253utyq21i.pdf

Face tracking and recognition with visual constraints in real-world videos

The method involves obtaining data representing a sample of stream and an associated temporal timestamp extracted by a playback device. A request containing the data is transmitted with a remote server (S). A given moment of trigger is obtained according to the temporal time stamp and another temporal time stamp for reception of extracted sample of stream received by the server. An action at a known moment is triggered, and an audio signal is extracted by the playback device. An independent claim is also included for a method for transmission of temporal information.

Method for triggering action on content of stream returned by broadcasting reproduction device i.e. TV, involves obtaining given moment of trigger according to temporal time stamps, and triggering action at known moment

The ACM AIEMPro 2011 workshop presents research on automated media content analysis and production for, amongst others, the development of novel TV services The program of the workshop has two sessions The first one is composed of three papers on video and TV content structuring and indexing The second session is also composed of three papers on media production and retrieval systems and applications

AIEMPro 2011: the 4th international workshop on automated media analysis and production for novel TV services

The invention relates to a method for merging segments of an audiovisual stream previously clipped into a plurality of program segments to be merged. According to the invention, such method includes, for at least one first and one second segment from said plurality of segments, a step of computing a set of descriptors, and includes a step of obtaining at least one piece of information representative of the fact that said at least one first and said at least one second segments belong to a same audiovisual program on the basis of data representative of said previously computed descriptors.

Method for merging audiovisual programs, and corresponding device and computer program product

The invention relates to a method of structuring a digital data stream comprising steps of: describing the stream by means of detailed descriptors and summary descriptors, accumulating, in the course of a given period, detailed descriptors and summary descriptors, grouping together accumulated detailed descriptors, forming repetitive sequences as a function of grouped detailed descriptors, extending repetitive sequences, as a function of summary descriptors accumulated during the given period, so as to form extended sequences, deleting the detailed descriptors and the summary descriptors that allowed the formation of the extended sequences.

Structuring of a digital data stream

This paper presents a content-based video copy detection system which achieves an optimal trade-off between effectiveness and efficiency. This is the most important feature for industrial applications such as copyright enforcement and duplicate detection in large databases.

Sid-Ahmed Berrani

Papers

Method for triggering action on content of stream returned by broadcasting reproduction device i.e. TV, involves obtaining given moment of trigger according to temporal time stamps, and triggering action at known moment

AIEMPro 2011: the 4th international workshop on automated media analysis and production for novel TV services

Method for merging audiovisual programs, and corresponding device and computer program product

Structuring of a digital data stream

A Real-Time Content Based Video Copy Detection System