NDLI: Deep Sentiment Features of Context and Faces for Affective Video Analysis

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Deep Sentiment Features of Context and Faces for Affective Video Analysis

Content Provider	ACM Digital Library
Author	Bertini, Marco Del Bimbo, Alberto Baecchi, Claudio Uricchio, Tiberio
Abstract	Given the huge quantity of hours of video available on video sharing platforms such as YouTube, Vimeo, etc. development of automatic tools that help users find videos that fit their interests has attracted the attention of both scientific and industrial communities. So far the majority of the works have addressed semantic analysis, to identify objects, scenes and events depicted in videos, but more recently affective analysis of videos has started to gain more attention. In this work we investigate the use of sentiment driven features to classify the induced sentiment of a video, i.e. the sentiment reaction of the user. Instead of using standard computer vision features such as CNN features or SIFT features trained to recognize objects and scenes, we exploit sentiment related features such as the ones provided by Deep-SentiBank, and features extracted from models that exploit deep networks trained on face expressions. We experiment on two recently introduced datasets: LIRIS-ACCEDE and MEDIAEVAL-2015, that provide sentiment annotations of a large set of short videos. We show that our approach not only outperforms the current state-of-the-art in terms of valence and arousal classification accuracy, but it also uses a smaller number of features, requiring thus less video processing.
Starting Page	72
Ending Page	77
Page Count	6
File Format	PDF
ISBN	9781450347013
DOI	10.1145/3078971.3079027
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-06-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Sentiment analysis Video analysis Affect recognition
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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