Recognizing the Emotions Evoked by Artworks Through Visual Features and Knowledge Graph-Embeddings

Aslan, Sinem; Castellano, Giovanna; Digeno, Vincenzo; Migailo, Giuseppe; Scaringi, Raffaele; Vessio, Gennaro

doi:10.1007/978-3-031-13321-3_12

Cited by 3 publications

(1 citation statement)

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“…In order to integrate the content information and the style information, Zhang et al proposed a novel CNN model to learn discriminative representation for image sentiment recognition [ 19 ]. Aslan et al proposed a new artistic knowledge graph to promote the emotion classification system, which exploits visual, contextual, and emotional information [ 20 ]. In addition to the CNN network, the recurrent neural network is also investigated in sentiment recognition.…”

Section: Related Workmentioning

confidence: 99%

Attention-Based Sentiment Region Importance and Relationship Analysis for Image Sentiment Recognition

Yang

Xing

Chang

et al. 2022

Computational Intelligence and Neuroscience

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Image sentiment recognition has attracted considerable attention from academia and industry due to the increasing tendency of expressing opinions via images and videos online. Previous studies focus on multilevel representation from global and local views to improve recognition performance. However, it is insufficient to research the importance and relationship of visual regions for image sentiment recognition. This paper proposes an attention-based sentiment region importance and relationship (ASRIR) analysis method, including important attention and relation attention for image sentiment recognition. First, we extract spatial region features using a multilevel pyramid network from the image. Second, we design important attention to exploring the sentiment semantic-related regions and relation attention to investigating the relationship between regions. In order to release the excessive concentration of attention, we employ a unimodal function as the objective function for regularization. Finally, the region features weighted by the attention mechanism are fused and input into a fully connected layer for classification. Extensive experiments on various commonly used image sentiment datasets demonstrate that our proposed method outperforms the state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%