Robust and efficient techniques for automatic video segmentation.

by Lam Cheung Fai.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 174-179).Abstract also in Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Problem Definition --- p.2Chapter 1.2 --- Motivation --- p.5Chapter 1.3 --- Problems --- p.7Chapter 1.3.1 --- Illumination Changes and Motions in Videos --- p.7Chapter 1.3.2 --- Variations in Video Scene Characteristics --- p.8Chapter 1.3.3 --- High Complexity of Algorithms --- p.10Chapter 1.3.4 --- Heterogeneous Approaches to Video Segmentation --- p.10Chapter 1.4 --- Objectives and Approaches --- p.11Chapter 1.5 --- Organization of the Thesis --- p.13Chapter 2 --- Related Work --- p.15Chapter 2.1 --- Algorithms for Uncompressed Videos --- p.16Chapter 2.1.1 --- Pixel-based Method --- p.16Chapter 2.1.2 --- Histogram-based Method --- p.17Chapter 2.1.3 --- Motion-based Algorithms --- p.18Chapter 2.1.4 --- Color-ratio Based Algorithms --- p.18Chapter 2.2 --- Algorithms for Compressed Videos --- p.19Chapter 2.2.1 --- Algorithms based on JPEG Image Sequences --- p.19Chapter 2.2.2 --- Algorithms based on MPEG Videos --- p.20Chapter 2.2.3 --- Algorithms based on VQ Compressed Videos --- p.21Chapter 2.3 --- Frame Difference Analysis Methods --- p.21Chapter 2.3.1 --- Scene Cut Detection --- p.21Chapter 2.3.2 --- Gradual Transition Detection --- p.22Chapter 2.4 --- Speedup Techniques --- p.23Chapter 2.5 --- Other Approaches --- p.24Chapter 3 --- Analysis and Enhancement of Existing Algorithms --- p.25Chapter 3.1 --- Introduction --- p.25Chapter 3.2 --- Video Segmentation Algorithms --- p.26Chapter 3.2.1 --- Frame Difference Metrics --- p.26Chapter 3.2.2 --- Frame Difference Analysis Methods --- p.29Chapter 3.3 --- Analysis of Feature Extraction Algorithms --- p.30Chapter 3.3.1 --- Pair-wise pixel comparison --- p.30Chapter 3.3.2 --- Color histogram comparison --- p.34Chapter 3.3.3 --- Pair-wise block-based comparison of DCT coefficients --- p.38Chapter 3.3.4 --- Pair-wise pixel comparison of DC-images --- p.42Chapter 3.4 --- Analysis of Scene Change Detection Methods --- p.45Chapter 3.4.1 --- Global Threshold Method --- p.45Chapter 3.4.2 --- Sliding Window Method --- p.46Chapter 3.5 --- Enhancements and Modifications --- p.47Chapter 3.5.1 --- Histogram Equalization --- p.49Chapter 3.5.2 --- DD Method --- p.52Chapter 3.5.3 --- LA Method --- p.56Chapter 3.5.4 --- Modification for pair-wise pixel comparison --- p.57Chapter 3.5.5 --- Modification for pair-wise DCT block comparison --- p.61Chapter 3.6 --- Conclusion --- p.69Chapter 4 --- Color Difference Histogram --- p.72Chapter 4.1 --- Introduction --- p.72Chapter 4.2 --- Color Difference Histogram --- p.73Chapter 4.2.1 --- Definition of Color Difference Histogram --- p.73Chapter 4.2.2 --- Sparse Distribution of CDH --- p.76Chapter 4.2.3 --- Resolution of CDH --- p.77Chapter 4.2.4 --- CDH-based Inter-frame Similarity Measure --- p.77Chapter 4.2.5 --- Computational Cost and Discriminating Power --- p.80Chapter 4.2.6 --- Suitability in Scene Change Detection --- p.83Chapter 4.3 --- Insensitivity to Illumination Changes --- p.89Chapter 4.3.1 --- Sensitivity of CDH --- p.90Chapter 4.3.2 --- Comparison with other feature extraction algorithms --- p.93Chapter 4.4 --- Orientation and Motion Invariant --- p.96Chapter 4.4.1 --- Camera Movements --- p.97Chapter 4.4.2 --- Object Motion --- p.100Chapter 4.4.3 --- Comparison with other feature extraction algorithms --- p.100Chapter 4.5 --- Performance of Scene Cut Detection --- p.102Chapter 4.6 --- Time Complexity Comparison --- p.105Chapter 4.7 --- Extension to DCT-compressed Images --- p.106Chapter 4.7.1 --- Performance of scene cut detection --- p.108Chapter 4.8 --- Conclusion --- p.109Chapter 5 --- Scene Change Detection --- p.111Chapter 5.1 --- Introduction --- p.111Chapter 5.2 --- Previous Approaches --- p.112Chapter 5.2.1 --- Scene Cut Detection --- p.112Chapter 5.2.2 --- Gradual Transition Detection --- p.115Chapter 5.3 --- DD Method --- p.116Chapter 5.3.1 --- Detecting Scene Cuts --- p.117Chapter 5.3.2 --- Detecting 1-frame Transitions --- p.121Chapter 5.3.3 --- Detecting Gradual Transitions --- p.129Chapter 5.4 --- Local Thresholding --- p.131Chapter 5.5 --- Experimental Results --- p.134Chapter 5.5.1 --- Performance of CDH+DD and CDH+DL --- p.135Chapter 5.5.2 --- Performance of DD on other features --- p.144Chapter 5.6 --- Conclusion --- p.150Chapter 6 --- Motion Vector Based Approach --- p.151Chapter 6.1 --- Introduction --- p.151Chapter 6.2 --- Previous Approaches --- p.152Chapter 6.3 --- MPEG-I Video Stream Format --- p.153Chapter 6.4 --- Derivation of Frame Differences from Motion Vector Counts --- p.156Chapter 6.4.1 --- Types of Frame Pairs --- p.156Chapter 6.4.2 --- Conditions for Scene Changes --- p.157Chapter 6.4.3 --- Frame Difference Measure --- p.159Chapter 6.5 --- Experiment --- p.160Chapter 6.5.1 --- Performance of MV --- p.161Chapter 6.5.2 --- Performance Enhancement --- p.162Chapter 6.5.3 --- Limitations --- p.163Chapter 6.6 --- Conclusion --- p.164Chapter 7 --- Conclusion and Future Work --- p.165Chapter 7.1 --- Contributions --- p.165Chapter 7.2 --- Future Work --- p.169Chapter 7.3 --- Conclusion --- p.171Bibliography --- p.174Chapter A --- Sample Videos --- p.180Chapter B --- List of Abbreviations --- p.18

Highly efficient low-level feature extraction for video representation and retrieval.

PhDWitnessing the omnipresence of digital video media, the research community has raised the question of its meaningful use and management. Stored in immense multimedia databases, digital videos need to be retrieved and structured in an intelligent way, relying on the content and the rich semantics involved. Current Content Based Video Indexing and Retrieval systems face the problem of the semantic gap between the simplicity of the available visual features and the richness of user semantics. This work focuses on the issues of efficiency and scalability in video indexing and retrieval to facilitate a video representation model capable of semantic annotation. A highly efficient algorithm for temporal analysis and key-frame extraction is developed. It is based on the prediction information extracted directly from the compressed domain features and the robust scalable analysis in the temporal domain. Furthermore, a hierarchical quantisation of the colour features in the descriptor space is presented. Derived from the extracted set of low-level features, a video representation model that enables semantic annotation and contextual genre classification is designed. Results demonstrate the efficiency and robustness of the temporal analysis algorithm that runs in real time maintaining the high precision and recall of the detection task. Adaptive key-frame extraction and summarisation achieve a good overview of the visual content, while the colour quantisation algorithm efficiently creates hierarchical set of descriptors. Finally, the video representation model, supported by the genre classification algorithm, achieves excellent results in an automatic annotation system by linking the video clips with a limited lexicon of related keywords

MAC-REALM: A video content feature extraction and modelling framework

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A consequence of the ‘data deluge’ is the exponential increase in digital video footage, while the ability to find relevant video clips diminishes. Traditional text based search engines are no longer optimal for searching, as they cannot provide a granular search of the content inside video footage. To be able to search the video in a content based manner, the content features of the video need to be extracted and modelled into a content model, which can then act as a searchable proxy for the video content. This thesis focuses on the extraction of syntactic and semantic content features and content modelling, using machine driven processes, with either little or no user interaction. Our abstract framework design extracts syntactic and semantic content features and compiles them into an integrated content model. The framework integrates a four plane strategy that consists of a pre-processing plane that removes redundant data and filters the media to improve the feature extraction properties of the media; a syntactic feature extraction plane that extracts low level syntactic feature and mid-level syntactic features that have semantic attributes; a semantic relationship analysis and linkage plane, where the spatial and temporal relationships of all the content features are defined, and finally a content modelling stage where the syntactic and semantic content features are integrated into a content model. Each of the four planes can be split into three layers namely, the content layer, where the content to be processed is stored; the application layer, where the content is converted into content descriptions, and the MPEG-7 layer, where content descriptions are serialised. Using MPEG-7 standards to produce the content model will provide wide-ranging interoperability, while facilitating granular multi-content type searches. The framework is aiming to ‘bridge’ the semantic gap, by integrating the syntactic and semantic content features from extraction through to modelling. The design of the framework has been implemented into a prototype called MAC-REALM, which has been tested and evaluated for its effectiveness to extract and model content features. Conclusions are drawn about the research output as a whole and whether they have met the objectives. Finally, future work is presented on how concept detection and crowd sourcing can be used with MAC-REALM

Detecção de algumas transições abruptas em sequencias de imagens

Orientador : Neucimar Jeronimo LeiteDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoMestrad

Adaptive video segmentation

The efficiency of a video indexing technique depends on the efficiency of the video segmentation algorithm which is a fundamental step in video indexing. Video segmentation is a process of splitting up a video sequence into its constituent scenes. This work focuses on the problem of video segmentation. A content-based approach has been used which segments a video based on the information extracted from the video itself. The main emphasis is on using structural information in the video such as edges as they are largely invariant to illumination and motion changes. The edge-based features have been used in conjunction with the intensity-based features in a multi-resolution framework to improve the performance of the segmentation algorithm.;To further improve the performance and to reduce the problem of automated choice of parameters, we introduce adaptation in the video segmentation process. (Abstract shortened by UMI.)

Feature based dynamic intra-video indexing

A thesis submitted in partial fulfillment for the degree of Doctor of PhilosophyWith the advent of digital imagery and its wide spread application in all vistas of life, it has become an important component in the world of communication. Video content ranging from broadcast news, sports, personal videos, surveillance, movies and entertainment and similar domains is increasing exponentially in quantity and it is becoming a challenge to retrieve content of interest from the corpora. This has led to an increased interest amongst the researchers to investigate concepts of video structure analysis, feature extraction, content annotation, tagging, video indexing, querying and retrieval to fulfil the requirements. However, most of the previous work is confined within specific domain and constrained by the quality, processing and storage capabilities. This thesis presents a novel framework agglomerating the established approaches from feature extraction to browsing in one system of content based video retrieval. The proposed framework significantly fills the gap identified while satisfying the imposed constraints of processing, storage, quality and retrieval times. The output entails a framework, methodology and prototype application to allow the user to efficiently and effectively retrieved content of interest such as age, gender and activity by specifying the relevant query. Experiments have shown plausible results with an average precision and recall of 0.91 and 0.92 respectively for face detection using Haar wavelets based approach. Precision of age ranges from 0.82 to 0.91 and recall from 0.78 to 0.84. The recognition of gender gives better precision with males (0.89) compared to females while recall gives a higher value with females (0.92). Activity of the subject has been detected using Hough transform and classified using Hiddell Markov Model. A comprehensive dataset to support similar studies has also been developed as part of the research process. A Graphical User Interface (GUI) providing a friendly and intuitive interface has been integrated into the developed system to facilitate the retrieval process. The comparison results of the intraclass correlation coefficient (ICC) shows that the performance of the system closely resembles with that of the human annotator. The performance has been optimised for time and error rate

A video summarisation system for post-production

Post-production facilities deal with large amounts of digital video, which presents difficulties when tracking, managing and searching this material. Recent research work in image and video analysis promises to offer help in these tasks, but there is a gap between what these systems can provide and what users actually need. In particular the popular research models for indexing and retrieving visual data do not fit well with how users actually work. In this thesis we explore how image and video analysis can be applied to an online video collection to assist users in reviewing and searching for material faster, rather than purporting to do it for them. We introduce a framework for automatically generating static 2-dimen- sional storyboards from video sequences. The storyboard consists of a series of frames, one for each shot in the sequence, showing the principal objects and motions of the shot. The storyboards are rendered as vector images in a familiar comic book style, allowing them to be quickly viewed and understood. The process consists of three distinct steps: shot-change detection, object segmentation, and presentation. The nature of the video material encountered in a post-production fa- cility is quite different from other material such as television programmes. Video sequences such as commercials and music videos are highly dy- namic with very short shots, rapid transitions and ambiguous edits. Video is often heavily manipulated, causing difficulties for many video processing techniques. We study the performance of a variety of published shot-change de- tection algorithms on the type of highly dynamic video typically encoun- tered in post-production work. Finding their performance disappointing, we develop a novel algorithm for detecting cuts and fades that operates directly on Motion-JPEG compressed video, exploiting the DCT coeffi- cients to save computation. The algorithm shows superior performance on highly dynamic material while performing comparably to previous algorithms on other material