The Focus–Aspect–Value model for predicting subjective visual attributes

Blandfort, Philipp; Karayil, Tushar; Hees, J.J. van; Dengel, Andreas

doi:10.1007/s13735-019-00188-5

Cited by 1 publication

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References 26 publications

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“…e recent studies enable reliable inference of high-level visual concepts, which provide a more robust midlevel representation for capturing higher-level semantics from images [11,12]. Leveraging semantic concepts related to visual content plays an important role in visual recognition, not only by providing effective clues for the generation of midlevel feature representation, but also requires fewer training examples.…”

Section: Introductionmentioning

confidence: 99%

Visual Emotion Analysis via Affective Semantic Concept Discovery

Zhu

et al. 2022

Scientific Programming

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With the development of social media, people prefer to express views and share daily life online via visual content, which has led to widespread attention in automatic emotion analysis from images. Capturing the emotions embedded in these social images has always been important yet challenging. In this paper, we propose a visual emotion prediction method that utilizes the affective semantic concepts of an image to predict its emotion. To solve the problems of narrow semantic coverage and low discriminative power of emotions in current semantic concept sets used for visual emotion analysis, we develop a concept selection model to mine emotion-related concepts from social media. Specifically, we propose several selection strategies to build an affective semantic concept set that contains various visual concepts related to emotion conveyance. And they are discovered from affective image datasets and associated tags crawled from websites. To further leverage the discovered affective semantic concepts, we train concept classifiers to predict the concept score of each concept, which are used as the intermediate features to tackle the semantic gap problem for image emotion recognition. Extensive experimental results confirm the validity of the affective semantic concepts and show the improved performance of our method.

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Section: Introductionmentioning

confidence: 99%

Visual Emotion Analysis via Affective Semantic Concept Discovery

Zhu

et al. 2022

Scientific Programming

View full text Add to dashboard Cite

show abstract

The Focus–Aspect–Value model for predicting subjective visual attributes

Cited by 1 publication

References 26 publications

Visual Emotion Analysis via Affective Semantic Concept Discovery

Visual Emotion Analysis via Affective Semantic Concept Discovery

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