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

This paper addresses the problem of unsupervised TV program structuring. Program structuring allows direct and non linear access to the desired parts of a program. Our work addresses the structuring of programs like news, entertainment, shows, magazines... It is based on the detection of audio and visual recurrences. It proposes an effective classification and selection system, based on decision trees, that allows the detection of “separators” among these recurrences. Separators are short audio/visual sequences that delimit the different parts of a program. The decision trees are built based on attributes issued from techniques like applause detection, scenes segmentation, face/speaker detection and clustering. The approach has been evaluated on a 112 hours dataset corresponding to 169 episodes of TV programs.

/pdf/audio-visual-recurrences-and-decision-trees-for-unsupervised-2zi3xl1xj9.pdf

Audio/visual recurrences and decision trees for unsupervised TV program structuring

This paper describes a new approach for performing efficient approximate k-nearest-neighbor searches in high-dimensional databases. This approach allows a fine and intuitive control over the precision of the search by setting at run time the maximum probability for a vector that would be in the exact answer set to be missing in the approximate set of answers. One of the contribution of this paper is an off-line process computing controlled approximations shrinking each cluster within which feature vectors are enclosed. Those approximations are values for (approximate) radiuses of clusters, and they are computed for all the levels of precision defined beforehand. Therefore, to answer a query, the search process simply considers the appropriate approximations corresponding to the desired level of precision. This may cause the actual nearest-neighbors of the query point to be ignored. Our method, however, probabilistically bounds the chances for this to happen. This paper also presents a performance study of the implementa- tion. It shows, for example, that our method is 6.72 times faster than the sequential scan when it handles more then 5 10^6 24-dimensions vectors, even when the probability of missing one of the true nearest-neighbors is below 0.01.

/pdf/approximate-k-nearest-neighbor-searches-a-new-algorithm-with-57ckct47ih.pdf

Approximate k-Nearest-Neighbor Searches: A New Algorithm with Probabilistic Control of the Precision

This paper focuses on automatic video summarization. We propose a novel modeling for summary creation using constraint satisfaction programming (CSP). The proposed modeling aims to provide the summarization method with more flexibility. It allows users to easily modify the expected summary depending on their preferences or the video type. Using this new modeling, constraints become easier to formulate. Moreover, the CSP solver explores more efficiently the search space. It provides more quickly better solutions. Our model is evaluated and compared with an existing modeling on tennis videos.

A Novel Modeling for Video Summarization Using Constraint Satisfaction Programming

The invention relates to a method for cutting, into at least two chapters, multimedia content comprising at least one video stream. According to the invention, such a method comprises: a stage of segmenting said video stream or streams into a plurality of shots which define scenes; a stage of analyzing similarities between scenes, applying at least one proximity signal to at least two analyzed scenes; a stage of constructing possible chapter cuts in said video stream on the basis of said proximity signals of said scenes and of at least one predetermined cut time constraint, which defines at least one signal characterizing the duration of a chapter; and a phase of constructing said chapters on the basis of said possible cuts..

Method for cutting multimedia content, and corresponding device and computer program

A method is provided for analyzing a piece of multimedia content including a time series of elementary entities, in order to check whether or not the piece of multimedia content includes at least one piece of reference content referenced in a content base. The method includes, for at least one elementary entity of the multimedia content and selected according to a predetermined selection rule, or current elementary entity: allocating a plausibility probability to each elementary entity of a set of current candidate elementary entities; allocating a transition probability between the current candidate elementary entity or entities and at least one previous candidate elementary entity; allocating an identification probability of at least one scenario including a time series of candidate elementary entities; deciding the recognition of one of the reference content and/or carrying on the analysis using a subsequent elementary entity of the piece of multimedia content.

Sid-Ahmed Berrani

Papers

Audio/visual recurrences and decision trees for unsupervised TV program structuring

Approximate k-Nearest-Neighbor Searches: A New Algorithm with Probabilistic Control of the Precision

A Novel Modeling for Video Summarization Using Constraint Satisfaction Programming

Method for cutting multimedia content, and corresponding device and computer program

Method for analyzing a multimedia content, corresponding computer program product and analysis device