Gossip-based computation of a Gaussian mixture model for distributed multimedia indexing

International audienceThe present paper deals with pattern recognition in a distributed computing context of the peer-to-peer type, that should be more and more interesting for multimedia data indexing and retrieval. Our goal is estimating of classconditional probability densities, that take the form of Gaussian mixture models (GMM). Originally, we propagate GMMs in a decentralized fashion (gossip) in a network, and aggregate GMMs from various sources, through a technique that only involves little computation and that makes parcimonious usage of the network resource, as model parameters rather than data are transmitted. The aggregation is based on iterative optimization of an approximation of a KL divergence allowing closed-form computation between mixture models. Experimental results demonstrate the scheme to the case of speaker recognition

Multi-Level Visual Alphabets

A central debate in visual perception theory is the argument for indirect versus direct perception; i.e., the use of intermediate, abstract, and hierarchical representations versus direct semantic interpretation of images through interaction with the outside world. We present a content-based representation that combines both approaches. The previously developed Visual Alphabet method is extended with a hierarchy of representations, each level feeding into the next one, but based on features that are not abstract but directly relevant to the task at hand. Explorative benchmark experiments are carried out on face images to investigate and explain the impact of the key parameters such as pattern size, number of prototypes, and distance measures used. Results show that adding an additional middle layer improves results, by encoding the spatial co-occurrence of lower-level pattern prototypes

Modelling Architecture for Multimedia Data Warehouse

ABSTRACT: Data Warehouse is an information system mainly used to support strategic decision. During last few years there is a need arise to manage multimedia data in decision making process in business industry which leads to build Multimedia data warehouse. Multimedia data warehouse is a collection of large volume of image, audio, video and text data. To efficiently store, access and analyse such data there is a need arise to manage these data. Data management includes the access and storage mechanisms that support the data warehouse. Storage and retrieval of multimedia data is a critical issue for the overall system's performance and functionality. Multimedia data warehouse must be studied in order to provide an efficient environment in which data can be efficiently stored, retrieved and analyzed. In this paper, we propose the architectural framework to build multimedia data warehouse with the aim to provide better performance. To achieve better storage, access and analysis performance certain techniques are incorporated. Storage efficiency is improved by using provided compression technique and partitioning method. Access and analysis efficiency is improved by representing multimedia data by multilevel features and by applying indexing technique

Human Activity Recognition in Real-Times Environments using Skeleton Joints

In this research work, we proposed a most effective noble approach for Human activity recognition in real-time environments. We recognize several distinct dynamic human activity actions using kinect. A 3D skeleton data is processed from real-time video gesture to sequence of frames and getter skeleton joints (Energy Joints, orientation, rotations of joint angles) from selected setof frames. We are using joint angle and orientations, rotations information from Kinect therefore less computation required. However, after extracting the set of frames we implemented several classification techniques Principal Component Analysis (PCA) with several distance based classifiers and Artificial Neural Network (ANN) respectively with some variants for classify our all different gesture models. However, we conclude that use very less number of frame (10-15%) for train our system efficiently from the entire set of gesture frames. Moreover, after successfully completion of our classification methods we clinch an excellent overall accuracy 94%, 96% and 98% respectively. We finally observe that our proposed system is more useful than comparing to other existing system, therefore our model is best suitable for real-time application such as in video games for player action/gesture recognition

Indexing Techniques for Image and Video Databases: an approach based on Animate Vision Paradigm

[ITALIANO]In questo lavoro di tesi vengono presentate e discusse delle innovative tecniche di indicizzazione per database video e di immagini basate sul paradigma della “Animate Vision” (Visione Animata). Da un lato, sarà mostrato come utilizzando, quali algoritmi di analisi di una data immagine, alcuni meccanismi di visione biologica, come i movimenti saccadici e le fissazioni dell'occhio umano, sia possibile ottenere un query processing in database di immagini più efficace ed efficiente. In particolare, verranno discussi, la metodologia grazie alla quale risulta possibile generare due sequenze di fissazioni, a partire rispettivamente, da un'immagine di query I_q ed una di test I_t del data set, e, come confrontare tali sequenze al fine di determinare una possibile misura della similarità (consistenza) tra le due immagini. Contemporaneamente, verrà discusso come tale approccio unito a tecniche classiche di clustering possa essere usato per scoprire le associazioni semantiche nascoste tra immagini, in termini di categorie, che, di contro, permettono un'automatica pre-classificazione (indicizzazione) delle immagini e possono essere usate per guidare e migliorare il processo di query. Saranno presentati, infine, dei risultati preliminari e l'approccio proposto sarà confrontato con le più recenti tecniche per il recupero di immagini descritte in letteratura. Dall'altro lato, sarà mostrato come utilizzando la precedente rappresentazione “foveata” di un'immagine, risulti possibile partizionare un video in shot. Più precisamente, il metodo per il rilevamento dei cambiamenti di shot si baserà sulla computazione, in ogni istante di tempo, della misura di consistenza tra le sequenze di fissazioni generate da un osservatore ideale che guarda il video. Lo schema proposto permette l'individuazione, attraverso l'utilizzo di un'unica tecnica anziché di più metodi dedicati, sia delle transizioni brusche sia di quelle graduali. Vengono infine mostrati i risultati ottenuti su varie tipologie di video e, come questi, validano l'approccio proposto. / [INGLESE]In this dissertation some novel indexing techniques for video and image database based on “Animate Vision” Paradigm are presented and discussed. From one hand, it will be shown how, by embedding within image inspection algorithms active mechanisms of biological vision such as saccadic eye movements and fixations, a more effective query processing in image database can be achieved. In particular, it will be discussed the way to generate two fixation sequences from a query image I_q and a test image I_t of the data set, respectively, and how to compare the two sequences in order to compute a possible similarity (consistency) measure between the two images. Meanwhile, it will be shown how the approach can be used with classical clustering techniques to discover and represent the hidden semantic associations among images, in terms of categories, which, in turn, allow an automatic pre-classification (indexing), and can be used to drive and improve the query processing. Eventually, preliminary results will be presented and the proposed approach compared with the most recent techniques for image retrieval described in the literature. From the other one, it will be discussed how by taking advantage of such foveated representation of an image, it is possible to partitioning of a video into shots. More precisely, the shot-change detection method will be based on the computation, at each time instant, of the consistency measure of the fixation sequences generated by an ideal observer looking at the video. The proposed scheme aims at detecting both abrupt and gradual transitions between shots using a single technique, rather than a set of dedicated methods. Results on videos of various content types are reported and validate the proposed approach

Fuzzy clustering for content-based indexing in multimedia databases.

Yue Ho-Yin.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 129-137).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Problem Definition --- p.7Chapter 1.2 --- Contributions --- p.8Chapter 1.3 --- Thesis Organization --- p.10Chapter 2 --- Literature Review --- p.11Chapter 2.1 --- "Content-based Retrieval, Background and Indexing Problem" --- p.11Chapter 2.1.1 --- Feature Extraction --- p.12Chapter 2.1.2 --- Nearest-neighbor Search --- p.13Chapter 2.1.3 --- Content-based Indexing Methods --- p.15Chapter 2.2 --- Indexing Problems --- p.25Chapter 2.3 --- Data Clustering Methods for Indexing --- p.26Chapter 2.3.1 --- Probabilistic Clustering --- p.27Chapter 2.3.2 --- Possibilistic Clustering --- p.34Chapter 3 --- Fuzzy Clustering Algorithms --- p.37Chapter 3.1 --- Fuzzy Competitive Clustering --- p.38Chapter 3.2 --- Sequential Fuzzy Competitive Clustering --- p.40Chapter 3.3 --- Experiments --- p.43Chapter 3.3.1 --- Experiment 1: Data set with different number of samples --- p.44Chapter 3.3.2 --- Experiment 2: Data set on different dimensionality --- p.46Chapter 3.3.3 --- Experiment 3: Data set with different number of natural clusters inside --- p.55Chapter 3.3.4 --- Experiment 4: Data set with different noise level --- p.56Chapter 3.3.5 --- Experiment 5: Clusters with different geometry size --- p.60Chapter 3.3.6 --- Experiment 6: Clusters with different number of data instances --- p.67Chapter 3.3.7 --- Experiment 7: Performance on real data set --- p.71Chapter 3.4 --- Discussion --- p.72Chapter 3.4.1 --- "Differences Between FCC, SFCC, and Others Clustering Algorithms" --- p.72Chapter 3.4.2 --- Variations on SFCC --- p.75Chapter 3.4.3 --- Why SFCC? --- p.75Chapter 4 --- Hierarchical Indexing based on Natural Clusters Information --- p.77Chapter 4.1 --- The Hierarchical Approach --- p.77Chapter 4.2 --- The Sequential Fuzzy Competitive Clustering Binary Tree (SFCC- b-tree) --- p.79Chapter 4.2.1 --- Data Structure of SFCC-b-tree --- p.80Chapter 4.2.2 --- Tree Building of SFCC-b-Tree --- p.82Chapter 4.2.3 --- Insertion of SFCC-b-tree --- p.83Chapter 4.2.4 --- Deletion of SFCC-b-Tree --- p.84Chapter 4.2.5 --- Searching in SFCC-b-Tree --- p.84Chapter 4.3 --- Experiments --- p.88Chapter 4.3.1 --- Experimental Setting --- p.88Chapter 4.3.2 --- Experiment 8: Test for different leaf node sizes --- p.90Chapter 4.3.3 --- Experiment 9: Test for different dimensionality --- p.97Chapter 4.3.4 --- Experiment 10: Test for different sizes of data sets --- p.104Chapter 4.3.5 --- Experiment 11: Test for different data distributions --- p.109Chapter 4.4 --- Summary --- p.113Chapter 5 --- A Case Study on SFCC-b-tree --- p.114Chapter 5.1 --- Introduction --- p.114Chapter 5.2 --- Data Collection --- p.115Chapter 5.3 --- Data Pre-processing --- p.116Chapter 5.4 --- Experimental Results --- p.119Chapter 5.5 --- Summary --- p.121Chapter 6 --- Conclusion --- p.122Chapter 6.1 --- An Efficiency Formula --- p.122Chapter 6.1.1 --- Motivation --- p.122Chapter 6.1.2 --- Regression Model --- p.123Chapter 6.1.3 --- Discussion --- p.124Chapter 6.2 --- Future Directions --- p.127Chapter 6.3 --- Conclusion --- p.128Bibliography --- p.12