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 automatic video summarization. The proposed solution relies on constraint satisfaction programming (CSP). Summary generation rules are expressed as constraints and the summary is created using the CSP solver given the input video, its audio-visual features and possibly user parameters (like the desired duration). The solution clearly separates production rules from the generation algorithm, which in practice allows users to easily express their constraints and preferences and also to modify them w.r.t. the target application. The solution is extensively evaluated in the context of tennis match summarization.

/pdf/constraint-satisfaction-programming-for-video-summarization-5ghlr01c60.pdf

Constraint Satisfaction Programming for Video Summarization

This paper proposes a method for classifying TV stream segments as long programs or inter-programs (IP). As almost all IPs are broadcasted several times in the TV stream, a first segmentation step based on a repeated sequence detection is performed. Resulting segments (the occurrences of repeated sequences and the rest of the stream) have to be classified. The proposed solution for that is based on Inductive Logic Programming. In addition to intrinsic features of each segment (e.g.duration), our technique makes use of the relational and contextual information of the segments in the stream. The effectiveness of our solution has been shown on a real TV stream of 6 days and a comparative study with an SVM-based classification approach has been performed.

An Inductive Logic Programming-Based Approach for TV Stream Segment Classification

This paper addresses the problem of unsupervised TV program structuring. Program structuring allows to improve browsing and interaction with TV programs. Our work addresses the structuring of recurrent TV programs like entertainment programs, shows, magazines... It relies on the automatic detection of separators in programs using a repeated sequence detection method that is applied over a set of episodes of the same TV program. Separators are short sequences that are inserted between different parts of a program and that are repeated between and/or within the episodes of the same program. The effectiveness of the approach has been evaluated over 54 episodes of daily TV programs.

An unsupervised approach for recurrent tv program structuring

La recherche d'images par le contenu au sein de grandes bases de donnees est un processus notoirement couteux Il s'avere que l'on peut fortement reduire ce cout si l'on effectue des recherches approximatives Cet article propose une nouvelle methode de recherche approximative de plus proches voisins (ppv) qui permet un controle fin de la precision de la recherche Ce controle s'exprime au travers d'un seul parametre qui indique la probabilite maximale de ne pas retrouver un des ppv exacts Nous montrons de plus que cette methode est particulierement bien adaptee au cas de la recherche d'images decrites par des descripteurs locaux Dans ce cas, la multiplicite des descripteurs par image compense totalement l'imprecision de la recherche

Recherche approximative de plus proches voisins

This paper addresses the problem of similarity searches within large databases of multimedia documents. It proposes an adaptive and parameter-free method for automatic decision making during the final step of the similarity search. This method computes the minimum number of features that have to be shared by two documents to be considered as similar. It is based on a probabilistic approach that uses an a contrario modelling of the similarity. The resulting decision threshold is well-adapted to the number of features in the database, the number of features in the query document and also their rareness. The method is applied to image and video copy detection, for which the features are the image local descriptors. Experiments show the effectiveness and efficiency of the proposed method.

Sid-Ahmed Berrani

Papers

Constraint Satisfaction Programming for Video Summarization

An Inductive Logic Programming-Based Approach for TV Stream Segment Classification

An unsupervised approach for recurrent tv program structuring

Recherche approximative de plus proches voisins

Adaptive similarity search in large databases - application to image/video copy detection