Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Visual sentiment prediction based on automatic discovery of affective regions

Yang, Jufeng, She, Songyu, Sun, Ming, Cheng, Ming-Ming, Rosin, Paul L, ORCID: and Wang, Liang 2018. Visual sentiment prediction based on automatic discovery of affective regions. IEEE Transactions on Multimedia 20 (9) , pp. 2513-2525. 10.1109/TMM.2018.2803520

[thumbnail of visualsentiment.pdf]
PDF - Accepted Post-Print Version
Download (1MB) | Preview


Automatic assessment of sentiment from visual content has gained considerable attention with the increasing tendency of expressing opinions via images and videos online. This paper investigates the problem of visual sentiment analysis, which involves a high-level abstraction in the recognition process. While most of the current methods focus on improving holistic representations, we aim to utilize the local information, which is inspired by the observation that both the whole image and local regions convey significant sentiment information. We propose a framework to leverage affective regions, where we first use an off-the-shelf objectness tool to generate the candidates, and employ a candidate selection method to remove redundant and noisy proposals. Then a convolutional neural network (CNN) is connected with each candidate to compute the sentiment scores, and the affective regions are automatically discovered, taking the objectness score as well as the sentiment score into consideration. Finally, the CNN outputs from local regions are aggregated with the whole images to produce the final predictions. Our framework only requires image-level labels, thereby significantly reducing the annotation burden otherwise required for training. This is especially important for sentiment analysis as sentiment can be abstract, and labeling affective regions is too subjective and labor-consuming. Extensive experiments show that the proposed algorithm outperforms the state-of-the-art approaches on eight popular benchmark datasets.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: IEEE
ISSN: 1520-9210
Date of First Compliant Deposit: 25 January 2018
Date of Acceptance: 22 January 2018
Last Modified: 06 Nov 2023 13:42

Citation Data

Cited 114 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics