Video summarization by group scoring

In this paper a new model for user-centered video summarization is presented. Involvement of more than one expert in generating the final video summary should be regarded as the main use case for this algorithm. This approach consists of three major steps. First, the video frames are scored by a group of operators. Next, these assigned scores are averaged to produce a singular value for each frame and lastly, the highest scored video frames alongside the corresponding audio and textual contents are extracted to be inserted into the summary. The effectiveness of this approach has been evaluated by comparing the video summaries generated by this system against the results from a number of automatic summarization tools that use different modalities for abstraction

Personalized video summarization by highest quality frames

In this work, a user-centered approach has been the basis for generation of the personalized video summaries. Primarily, the video experts score and annotate the video frames during the enrichment phase. Afterwards, the frames scores for different video segments will be updated based on the captured end-users (different with video experts) priorities towards existing video scenes. Eventually, based on the pre-defined skimming time, the highest scored video frames will be extracted to be included into the personalized video summaries. In order to evaluate the effectiveness of our proposed model, we have compared the video summaries generated by our system against the results from 4 other summarization tools using different modalities

From media crossing to media mining

This paper reviews how the concept of Media Crossing has contributed to the advancement of the application domain of information access and explores directions for a future research agenda. These will include themes that could help to broaden the scope and to incorporate the concept of medium-crossing in a more general approach that not only uses combinations of medium-specific processing, but that also exploits more abstract medium-independent representations, partly based on the foundational work on statistical language models for information retrieval. Three examples of successful applications of media crossing will be presented, with a focus on the aspects that could be considered a first step towards a generalized form of media mining

User-centred video abstraction

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThe rapid growth of digital video content in recent years has imposed the need for the development of technologies with the capability to produce condensed but semantically rich versions of the input video stream in an effective manner. Consequently, the topic of Video Summarisation is becoming increasingly popular in multimedia community and numerous video abstraction approaches have been proposed accordingly. These recommended techniques can be divided into two major categories of automatic and semi-automatic in accordance with the required level of human intervention in summarisation process. The fully-automated methods mainly adopt the low-level visual, aural and textual features alongside the mathematical and statistical algorithms in furtherance to extract the most significant segments of original video. However, the effectiveness of this type of techniques is restricted by a number of factors such as domain-dependency, computational expenses and the inability to understand the semantics of videos from low-level features. The second category of techniques however, attempts to alleviate the quality of summaries by involving humans in the abstraction process to bridge the semantic gap. Nonetheless, a single user’s subjectivity and other external contributing factors such as distraction will potentially deteriorate the performance of this group of approaches. Accordingly, in this thesis we have focused on the development of three user-centred effective video summarisation techniques that could be applied to different video categories and generate satisfactory results. According to our first proposed approach, a novel mechanism for a user-centred video summarisation has been presented for the scenarios in which multiple actors are employed in the video summarisation process in order to minimise the negative effects of sole user adoption. Based on our recommended algorithm, the video frames were initially scored by a group of video annotators ‘on the fly’. This was followed by averaging these assigned scores in order to generate a singular saliency score for each video frame and, finally, the highest scored video frames alongside the corresponding audio and textual contents were extracted to be included into the final summary. The effectiveness of our approach has been assessed by comparing the video summaries generated based on our approach against the results obtained from three existing automatic summarisation tools that adopt different modalities for abstraction purposes. The experimental results indicated that our proposed method is capable of delivering remarkable outcomes in terms of Overall Satisfaction and Precision with an acceptable Recall rate, indicating the usefulness of involving user input in the video summarisation process. In an attempt to provide a better user experience, we have proposed our personalised video summarisation method with an ability to customise the generated summaries in accordance with the viewers’ preferences. Accordingly, the end-user’s priority levels towards different video scenes were captured and utilised for updating the average scores previously assigned by the video annotators. Finally, our earlier proposed summarisation method was adopted to extract the most significant audio-visual content of the video. Experimental results indicated the capability of this approach to deliver superior outcomes compared with our previously proposed method and the three other automatic summarisation tools. Finally, we have attempted to reduce the required level of audience involvement for personalisation purposes by proposing a new method for producing personalised video summaries. Accordingly, SIFT visual features were adopted to identify the video scenes’ semantic categories. Fusing this retrieved data with pre-built users’ profiles, personalised video abstracts can be created. Experimental results showed the effectiveness of this method in delivering superior outcomes comparing to our previously recommended algorithm and the three other automatic summarisation techniques

Deep Cross-Modal Correlation Learning for Audio and Lyrics in Music Retrieval

Deep cross-modal learning has successfully demonstrated excellent performance in cross-modal multimedia retrieval, with the aim of learning joint representations between different data modalities. Unfortunately, little research focuses on cross-modal correlation learning where temporal structures of different data modalities such as audio and lyrics should be taken into account. Stemming from the characteristic of temporal structures of music in nature, we are motivated to learn the deep sequential correlation between audio and lyrics. In this work, we propose a deep cross-modal correlation learning architecture involving two-branch deep neural networks for audio modality and text modality (lyrics). Data in different modalities are converted to the same canonical space where inter modal canonical correlation analysis is utilized as an objective function to calculate the similarity of temporal structures. This is the first study that uses deep architectures for learning the temporal correlation between audio and lyrics. A pre-trained Doc2Vec model followed by fully-connected layers is used to represent lyrics. Two significant contributions are made in the audio branch, as follows: i) We propose an end-to-end network to learn cross-modal correlation between audio and lyrics, where feature extraction and correlation learning are simultaneously performed and joint representation is learned by considering temporal structures. ii) As for feature extraction, we further represent an audio signal by a short sequence of local summaries (VGG16 features) and apply a recurrent neural network to compute a compact feature that better learns temporal structures of music audio. Experimental results, using audio to retrieve lyrics or using lyrics to retrieve audio, verify the effectiveness of the proposed deep correlation learning architectures in cross-modal music retrieval