Paper Info
Title
Object-Based Visual Sentiment Concept Analysis and Application
Abstract
This paper studies the problem of modeling object-based visual concepts such as \"crazy car\" and \"shy dog\" with a goal to extract emotion related information from social multimedia content. We focus on detecting such adjective-noun pairs because of their strong co-occurrence relation with image tags about emotions. This problem is very challenging due to the highly subjective nature of the adjectives like \"crazy\" and \"shy\" and the ambiguity associated with the annotations. However, associating adjectives with concrete physical nouns makes the combined visual concepts more detectable and tractable. We propose a hierarchical system to handle the concept classification in an object specific manner and decompose the hard problem into object localization and sentiment related concept modeling. In order to resolve the ambiguity of concepts we propose a novel classification approach by modeling the concept similarity, leveraging on online commonsense knowledgebase. The proposed framework also allows us to interpret the classifiers by discovering discriminative features. The comparisons between our method and several baselines show great improvement in classification performance. We further demonstrate the power of the proposed system with a few novel applications such as sentiment-aware music slide shows of personal albums.
Year
DOI
Venue
2014
10.1145/2647868.2654935
ACM Multimedia 2001
Keywords
Field
DocType
affective computing,information search and retrieval,social multimedia,visual sentiment
Hierarchical control system,Computer vision,Computer science,Noun,Baseline (configuration management),Social multimedia,Artificial intelligence,Affective computing,Discriminative model,Ambiguity,Formal concept analysis
Conference
Citations 
PageRank 
References 
47
1.14
30
Authors
6
Name
Order
Citations
PageRank
Tao Chen159929.93
Felix X. Yu256025.80
Jiawei Chen38714.73
Yin Cui426211.30
Yan-Ying Chen532820.84
Shih-Fu Chang6130151101.53