Special Issue "Multimodal Deep Learning Methods for Video Analytics"
Deadline for manuscript submissions: closed (15 December 2019).
Interests: databases; big data analysis; music retrieval; multimedia systems; machine learning; knowledge management; computational intelligence
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The presence of video capturing devices is ubiquitous in the current era. The nature and range of video data now virtually covers all aspects of our daily lives. The wide variety of videos captured by video capturing devices include edited videos (movies, serials, etc.) at one end and a huge amount of unedited content (consumer videos, ego-centric videos, etc.) on the other end. Because of this ubiquitous presence of video capturing devices, the videos now contain rich information and knowledge which can be extracted and analyzed for a variety of applications. Video analytics is a broad field which encompasses the design and development of the systems having the capability to automatically analyze the videos for the detection of spatial and temporal events of interest.
In the last few years, deep learning algorithms have shown tremendous performance in many research areas especially computer vision and natural language processing (NLP). The deep learning-based algorithms have attained such remarkable performance in tasks like image recognition, speech recognition and NLP which was beyond expectation a decade ago. In multimodal deep learning, the data is obtained from different sources and then used to learn features over multiple modalities. This helps in generation of a shared representation between different modalities. It is expected that the usage of multiple modalities results in superior performance. An example of usage of multiple modalities in video analytics is the usage of audio, visual and (possibly) textual data for the sake of analysis.
The objectives of this Special Issue are to gather work done in video analytics using multimodal deep learning-based methods and to introduce work done on large scale new real-world applications of video analytics.
We solicit original research and survey papers addressing the topics including (but not limited to):
- Analysis of first-person/wearable videos using multimodal deep learning techniques,
- Analysis of web videos, ego-centric videos, surveillance videos, movies or any other type of videos using multimodal deep learning techniques,
- Data collections, benchmarking, and performance evaluation of deep learning-based video analytics.
- Multimodal deep convolutional neural network for audio-visual emotion recognition
- Multimodal deep learning framework with cross weights
- Multimodal information fusion via deep learning or machine learning methods
The topics in video analytics may include (but are not limited to):
- Object detection and recognition
- Action recognition
- Event detection
- Video highlights, summary and storyboard generation
- Segmentation and tracking
- Authoring and editing of videos
- Scene understanding
- People analysis
- Security issues in surveillance videos
Dr. Seungmin Rho
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Audio-Visual Emotion Recognition
- Deep Learning
- Natural Language Processing
- Video Analytics