Affect-based indexing and retrieval of multimedia data

Digital multimedia systems are creating many new opportunities for rapid access to content archives. In order to explore these collections using search, the content must be annotated with significant features. An important and often overlooked aspect o f human interpretation o f multimedia data is the affective dimension. The hypothesis o f this thesis is that affective labels o f content can be extracted automatically from within multimedia data streams, and that these can then be used for content-based retrieval and browsing. A novel system is presented for extracting affective features from video content and mapping it onto a set o f keywords with predetermined emotional interpretations. These labels are then used to demonstrate affect-based retrieval on a range o f feature films. Because o f the subjective nature o f the words people use to describe emotions, an approach towards an open vocabulary query system utilizing the electronic lexical database WordNet is also presented. This gives flexibility for search queries to be extended to include keywords without predetermined emotional interpretations using a word-similarity measure. The thesis presents the framework and design for the affectbased indexing and retrieval system along with experiments, analysis, and conclusions

Indexing, browsing and searching of digital video

Video is a communications medium that normally brings together moving pictures with a synchronised audio track into a discrete piece or pieces of information. The size of a “piece ” of video can variously be referred to as a frame, a shot, a scene, a clip, a programme or an episode, and these are distinguished by their lengths and by their composition. We shall return to the definition of each of these in section 4 this chapter. In modern society, video is ver

Video Indexing and Retrieval Techniques Using Novel Approaches to Video Segmentation, Characterization, and Similarity Matching

Multimedia applications are rapidly spread at an ever-increasing rate introducing a number of challenging problems at the hands of the research community, The most significant and influential problem, among them, is the effective access to stored data. In spite of the popularity of keyword-based search technique in alphanumeric databases, it is inadequate for use with multimedia data due to their unstructured nature. On the other hand, a number of content-based access techniques have been developed in the context of image indexing and retrieval; meanwhile video retrieval systems start to gain wide attention, This work proposes a number of techniques constituting a fully content-based system for retrieving video data. These techniques are primarily targeting the efficiency, reliability, scalability, extensibility, and effectiveness requirements of such applications. First, an abstract representation of the video stream, known as the DC sequence, is extracted. Second, to deal with the problem of video segmentation, an efficient neural network model is introduced. The novel use of the neural network improves the reliability while the efficiency is achieved through the instantaneous use of the recall phase to identify shot boundaries. Third, the problem of key frames extraction is addressed using two efficient algorithms that adapt their selection decisions based on the amount of activity found in each video shot enabling the selection of a near optimal expressive set of key frames. Fourth, the developed system employs an indexing scheme that supports two low-level features, color and texture, to represent video data, Finally, we propose, in the retrieval stage, a novel model for performing video data matching task that integrates a number of human-based similarity factors. All our software implementations are in Java, which enables it to be used across heterogeneous platforms. The retrieval system performance has been evaluated yielding a very good retrieval rate and accuracy, which demonstrate the effectiveness of the developed system

Automated Organisation and Quality Analysis of User-Generated Audio Content

The abundance and ubiquity of user-generated content has opened horizons when it comes to the organization and analysis of vast and heterogeneous data, especially with the increase of quality of the recording devices witnessed nowadays. Most of the activity experienced in social networks today contains audio excerpts, either by belonging to a certain video file or an actual audio clip, therefore the analysis of the audio features present in such content is of extreme importance in order to better understand it. Such understanding would lead to a better handling of ubiquity data and would ultimately provide a better experience to the end-user. The work discussed in this thesis revolves around using audio features to organize and retrieve meaningful insights from user-generated content crawled from social media websites, more particularly data related to concert clips. From its redundancy and abundance (i.e., for the existence of several recordings of a given event), recordings from musical shows represent a very good use case to derive useful and practical conclusions around the scope of this thesis. Mechanisms that provide a better understanding of such content are presented and already partly implemented, such as audio clustering based on the existence of overlapping audio segments between different audio clips, audio segmentation that synchronizes and relates the different cluster’s clips in time, and techniques to infer audio quality of such clips. All the proposed methods use information retrieved from an audio fingerprinting algorithm, used for the synchronization of the different audio files, with methods for filtering possible false positives of the algorithm being also presented. For the evaluation and validation of the proposed methods, we used one dataset made of several audio recordings regarding different concert clips manually crawled from YouTube

Multimedia authoring, development environments, and digital video editing

Multimedia systems integrate text, audio, video, graphics, and other media and allow them to be utilized in a combined and interactive manner. Using this exciting and rapidly developing technology, multimedia applications can provide extensive benefits in a variety of arenas, including research, education, medicine, and commerce. While there are many commercial multimedia development packages, the easy and fast creation of a useful, full-featured multimedia document is not yet a straightforward task. This paper addresses issues in the development of multimedia documents, ranging from user-interface tools that manipulate multimedia documents to multimedia communication technologies such as compression, digital video editing and information retrieval. It outlines the basic steps in the multimedia authoring process and some of the requirements that need to be met by multimedia development environments. It also presents the role of video, an essential component of multimedia systems and the role of programming in digital video editing. A model is described for remote access of distributed video. The paper concludes with a discussion of future research directions and new uses of multimedia documents

Automated generation of movie tributes

O objetivo desta tese é gerar um tributo a um filme sob a forma de videoclip, considerando como entrada um filme e um segmento musical coerente. Um tributo é considerado um vídeo que contém os clips mais significativos de um filme, reproduzidos sequencialmente, enquanto uma música toca. Nesta proposta, os clips a constar do tributo final são o resultado da sumarização das legendas do filme com um algoritmo de sumarização genérico. É importante que o artefacto seja coerente e fluido, pelo que há a necessidade de haver um equilíbrio entre a seleção de conteúdo importante e a seleção de conteúdo que esteja em harmonia com a música. Para tal, os clips são filtrados de forma a garantir que apenas aqueles que contêm a mesma emoção da música aparecem no vídeo final. Tal é feito através da extração de vetores de características áudio relacionadas com emoções das cenas às quais os clips pertencem e da música, e, de seguida, da sua comparação por meio do cálculo de uma medida de distância. Por fim, os clips filtrados preenchem a música cronologicamente. Os resultados foram positivos: em média, os tributos produzidos obtiveram 7 pontos, numa escala de 0 a 10, em critérios como seleção de conteúdo e coerência emocional, fruto de avaliação humana.This thesis’ purpose is to generate a movie tribute in the form of a videoclip for a given movie and music. A tribute is considered to be a video containing meaningful clips from the movie playing along with a cohesive music piece. In this work, we collect the clips by summarizing the movie subtitles with a generic summarization algorithm. It is important that the artifact is coherent and fluid, hence there is the need to balance between the selection of important content and the selection of content that is in harmony with the music. To achieve so, clips are filtered so as to ensure that only those that contain the same emotion as the music are chosen to appear in the final video. This is made by extracting vectors of emotion-related audio features from the scenes they belong to and from the music, and then comparing them with a distance measure. Finally, filtered clips fill the music length in a chronological order. Results were positive: on average, the produced tributes obtained scores of 7, on a scale from 0 to 10, on content selection, and emotional coherence criteria, from human evaluation

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

Image Annotation and Topic Extraction Using Super-Word Latent Dirichlet

This research presents a multi-domain solution that uses text and images to iteratively improve automated information extraction. Stage I uses local text surrounding an embedded image to provide clues that help rank-order possible image annotations. These annotations are forwarded to Stage II, where the image annotations from Stage I are used as highly-relevant super-words to improve extraction of topics. The model probabilities from the super-words in Stage II are forwarded to Stage III where they are used to refine the automated image annotation developed in Stage I. All stages demonstrate improvement over existing equivalent algorithms in the literature