NDLI: Learning deep features for image emotion classification

Content Provider	IEEE Xplore Digital Library
Author	Ming Chen Lu Zhang Allebach, J.P.
Copyright Year	2015
Description	Author affiliation: Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA (Ming Chen; Lu Zhang; Allebach, J.P.)
Abstract	Images can both express and affect people's emotions. It is intriguing and important to understand what emotions are conveyed and how they are implied by the visual content of images. Inspired by the recent success of deep convolutional neural networks (CNN) in visual recognition, we explore two simple, yet effective deep learning-based methods for image emotion analysis. The first method uses off-the-shelf CNN features directly for classification. For the second method, we fine-tune a CNN that is pre-trained on a large dataset, i.e. ImageNet, on our target dataset first. Then we extract features using the fine-tuned CNN at different location at multiple levels to capture both the global and local information. The features at different location are aggregated using the Fisher Vector for each level and concatenated to form a compact representation. From our experimental results, both the deep learning-based methods outperforms traditional methods based on generic image descriptors and hand-crafted features.
Starting Page	4491
Ending Page	4495
File Size	3947221
Page Count	5
File Format	PDF
e-ISBN	9781479983391
DOI	10.1109/ICIP.2015.7351656
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Training Visualization Image recognition Support vector machines Neural networks Machine learning convolutional neural network Image emotion image classification deep learning
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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