Gradual transition detection using color coherence and other criteria in a video shot meta-segmentation framework

Shot segmentation provides the basis for almost all high-level video content analysis approaches, validating it as one of the major prerequisites for efficient video semantic analysis, in-dexing and retrieval. The successful detection of both gradual and abrupt transitions is necessary to this end. In this pa-per a new gradual transition detection algorithm is proposed, that is based on novel criteria such as color coherence change that exhibit less sensitivity to local or global motion than pre-viously proposed ones. These criteria, each of which could serve as a standalone gradual transition detection approach, are then combined using a machine learning technique, to result in a meta-segmentation scheme. Besides significantly improved performance, advantage of the proposed scheme is that there is no need for threshold selection, as opposed to what would be the case if any of the proposed features were used by themselves and as is typically the case in the rele-vant literature. Performance evaluation and comparison with four other popular algorithms reveals the effectiveness of the proposed technique. Index Terms — video shot segmentation, gradual transi-tion, color coherence change, meta-segmentation 1

Deliverable D1.4 Visual, text and audio information analysis for hypervideo, final release

Having extensively evaluated the performance of the technologies included in the first release of WP1 multimedia analysis tools, using content from the LinkedTV scenarios and by participating in international benchmarking activities, concrete decisions regarding the appropriateness and the importance of each individual method or combination of methods were made, which, combined with an updated list of information needs for each scenario, led to a new set of analysis requirements that had to be addressed through the release of the final set of analysis techniques of WP1. To this end, coordinated efforts on three directions, including (a) the improvement of a number of methods in terms of accuracy and time efficiency, (b) the development of new technologies and (c) the definition of synergies between methods for obtaining new types of information via multimodal processing, resulted in the final bunch of multimedia analysis methods for video hyperlinking. Moreover, the different developed analysis modules have been integrated into a web-based infrastructure, allowing the fully automatic linking of the multitude of WP1 technologies and the overall LinkedTV platform

Iconic Indexing for Video Search

Submitted for the degree of Doctor of Philosophy, Queen Mary, University of London

SHOT SEGMENTATION FOR CONTENT BASED VIDEO RETRIEVAL

Content based video retrieval is a technique used to search and browse large collections of videos stored in a database. This technique has proven to be useful for numerous applications spreadacross different domains like, surveillance, security, biomedicine, and traffic regulation. Here, the analysis is carried out based on certain properties extracted from the video frames such as colour, edge, motion, and texture. Instead of storing the features of every single frame of the video, only the features of the representative frames,which describethe entire video, are stored. This results in better storage memory utilization. We propose a method which aims at efficiently segmenting the video into shots and selecting the key frames of each shot accordingly. In order to determine the shot boundaries, we have incorporated Colour Histogram and Background Subtraction methods in this paper.The analysis of the proposed technique is carried out for different videos

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

Deliverable D1.2 Visual, text and audio information analysis for hypervideo, first release

Enriching videos by offering continuative and related information via, e.g., audiostreams, web pages, as well as other videos, is typically hampered by its demand for massive editorial work. While there exist several automatic and semi-automatic methods that analyze audio/video content, one needs to decide which method offers appropriate information for our intended use-case scenarios. We review the technology options for video analysis that we have access to, and describe which training material we opted for to feed our algorithms. For all methods, we offer extensive qualitative and quantitative results, and give an outlook on the next steps within the project

Segmentation sémantique des contenus audio-visuels

Dans ce travail, nous avons mis au point une méthode de segmentation des contenus audiovisuels applicable aux appareils de stockage domestiques pour cela nous avons expérimenté un système distribué pour l’analyse du contenu composé de modules individuels d’analyse : les Service Unit. L’un d’entre eux a été dédié à la caractérisation des éléments hors contenu, i.e. les publicités, et offre de bonnes performances. Parallèlement, nous avons testé différents détecteurs de changement de plans afin de retenir le meilleur d’entre eux pour la suite. Puis, nous avons proposé une étude des règles de production des films, i.e. grammaire de films, qui a permis de définir les séquences de Parallel Shot. Nous avons, ainsi, testé quatre méthodes de regroupement basées similarité afin de retenir la meilleure d’entre elles pour la suite. Finalement, nous avons recherché différentes méthodes de détection des frontières de scènes et avons obtenu les meilleurs résultats en combinant une méthode basée couleur avec un critère de longueur de plan. Ce dernier offre des performances justifiant son intégration dans les appareils de stockage grand public.In this work we elaborated a method for semantic segmentation of audiovisual content applicable for consumer electronics storage devices. For the specific solution we researched first a service-oriented distributed multimedia content analysis framework composed of individual content analysis modules, i.e. Service Units. One of the latter was dedicated to identify non-content related inserts, i.e. commercials blocks, which reached high performance results. In a subsequent step we researched and benchmarked various Shot Boundary Detectors and implement the best performing one as Service Unit. Here after, our study of production rules, i.e. film grammar, provided insights of Parallel Shot sequences, i.e. Cross-Cuttings and Shot-Reverse-Shots. We researched and benchmarked four similarity-based clustering methods, two colour- and two feature-point-based ones, in order to retain the best one for our final solution. Finally, we researched several audiovisual Scene Boundary Detector methods and achieved best results combining a colour-based method with a shot length based criteria. This Scene Boundary Detector identified semantic scene boundaries with a robustness of 66% for movies and 80% for series, which proofed to be sufficient for our envisioned application Advanced Content Navigation

Annotation of multimedia learning materials for semantic search

Multimedia is the main source for online learning materials, such as videos, slides and textbooks, and its size is growing with the popularity of online programs offered by Universities and Massive Open Online Courses (MOOCs). The increasing amount of multimedia learning resources available online makes it very challenging to browse through the materials or find where a specific concept of interest is covered. To enable semantic search on the lecture materials, their content must be annotated and indexed. Manual annotation of learning materials such as videos is tedious and cannot be envisioned for the growing quantity of online materials. One of the most commonly used methods for learning video annotation is to index the video, based on the transcript obtained from translating the audio track of the video into text. Existing speech to text translators require extensive training especially for non-native English speakers and are known to have low accuracy. This dissertation proposes to index the slides, based on the keywords. The keywords extracted from the textbook index and the presentation slides are the basis of the indexing scheme. Two types of lecture videos are generally used (i.e., classroom recording using a regular camera or slide presentation screen captures using specific software) and their quality varies widely. The screen capture videos, have generally a good quality and sometimes come with metadata. But often, metadata is not reliable and hence image processing techniques are used to segment the videos. Since the learning videos have a static background of slide, it is challenging to detect the shot boundaries. Comparative analysis of the state of the art techniques to determine best feature descriptors suitable for detecting transitions in a learning video is presented in this dissertation. The videos are indexed with keywords obtained from slides and a correspondence is established by segmenting the video temporally using feature descriptors to match and align the video segments with the presentation slides converted into images. The classroom recordings using regular video cameras often have poor illumination with objects partially or totally occluded. For such videos, slide localization techniques based on segmentation and heuristics is presented to improve the accuracy of the transition detection. A region prioritized ranking mechanism is proposed that integrates the keyword location in the presentation into the ranking of the slides when searching for a slide that covers a given keyword. This helps in getting the most relevant results first. With the increasing size of course materials gathered online, a user looking to understand a given concept can get overwhelmed. The standard way of learning and the concept of “one size fits all” is no longer the best way to learn for millennials. Personalized concept recommendation is presented according to the user’s background knowledge. Finally, the contributions of this dissertation have been integrated into the Ultimate Course Search (UCS), a tool for an effective search of course materials. UCS integrates presentation, lecture videos and textbook content into a single platform with topic based search capabilities and easy navigation of lecture materials

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

Audio-visual football video analysis, from structure detection to attention analysis

Sport video is an important video genre. Content-based sports video analysis attracts great interest from both industry and academic fields. A sports video is characterised by repetitive temporal structures, relatively plain contents, and strong spatio-temporal variations, such as quick camera switches and swift local motions. It is necessary to develop specific techniques for content-based sports video analysis to utilise these characteristics. For an efficient and effective sports video analysis system, there are three fundamental questions: (1) what are key stories for sports videos; (2) what incurs viewer’s interest; and (3) how to identify game highlights. This thesis is developed around these questions. We approached these questions from two different perspectives and in turn three research contributions are presented, namely, replay detection, attack temporal structure decomposition, and attention-based highlight identification. Replay segments convey the most important contents in sports videos. It is an efficient approach to collect game highlights by detecting replay segments. However, replay is an artefact of editing, which improves with advances in video editing tools. The composition of replay is complex, which includes logo transitions, slow motions, viewpoint switches and normal speed video clips. Since logo transition clips are pervasive in game collections of FIFA World Cup 2002, FIFA World Cup 2006 and UEFA Championship 2006, we take logo transition detection as an effective replacement of replay detection. A two-pass system was developed, including a five-layer adaboost classifier and a logo template matching throughout an entire video. The five-layer adaboost utilises shot duration, average game pitch ratio, average motion, sequential colour histogram and shot frequency between two neighbouring logo transitions, to filter out logo transition candidates. Subsequently, a logo template is constructed and employed to find all transition logo sequences. The precision and recall of this system in replay detection is 100% in a five-game evaluation collection. An attack structure is a team competition for a score. Hence, this structure is a conceptually fundamental unit of a football video as well as other sports videos. We review the literature of content-based temporal structures, such as play-break structure, and develop a three-step system for automatic attack structure decomposition. Four content-based shot classes, namely, play, focus, replay and break were identified by low level visual features. A four-state hidden Markov model was trained to simulate transition processes among these shot classes. Since attack structures are the longest repetitive temporal unit in a sports video, a suffix tree is proposed to find the longest repetitive substring in the label sequence of shot class transitions. These occurrences of this substring are regarded as a kernel of an attack hidden Markov process. Therefore, the decomposition of attack structure becomes a boundary likelihood comparison between two Markov chains. Highlights are what attract notice. Attention is a psychological measurement of “notice ”. A brief survey of attention psychological background, attention estimation from vision and auditory, and multiple modality attention fusion is presented. We propose two attention models for sports video analysis, namely, the role-based attention model and the multiresolution autoregressive framework. The role-based attention model is based on the perception structure during watching video. This model removes reflection bias among modality salient signals and combines these signals by reflectors. The multiresolution autoregressive framework (MAR) treats salient signals as a group of smooth random processes, which follow a similar trend but are filled with noise. This framework tries to estimate a noise-less signal from these coarse noisy observations by a multiple resolution analysis. Related algorithms are developed, such as event segmentation on a MAR tree and real time event detection. The experiment shows that these attention-based approach can find goal events at a high precision. Moreover, results of MAR-based highlight detection on the final game of FIFA 2002 and 2006 are highly similar to professionally labelled highlights by BBC and FIFA