User-Centric Affective Computing of Image Emotion Perceptions
Authors: Sicheng Zhao, Hongxun Yao, Wenlong Xie, Xiaolei Jiang
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For evaluation, we set up a large scale image emotion dataset from Flickr, named Image-Emotion-Social-Net, with over 1 million images and about 8,000 users. Experiments conducted on this dataset demonstrate the superiority of the proposed method, as compared to state-of-the-art. |
| Researcher Affiliation | Academia | Sicheng Zhao, Hongxun Yao, Wenlong Xie, Xiaolei Jiang School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. zsc@hit.edu.cn; h.yao@hit.edu.cn; wlxie@hit.edu.cn; xljiang@hit.edu.cn |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found in the paper. |
| Open Source Code | No | The paper provides a link to an Appendix: 'https://sites.google.com/site/schzhao/'. This is a personal website and not a direct link to a source-code repository for the methodology described in the paper, nor does the paper explicitly state that the code itself is provided at this link. |
| Open Datasets | No | The paper describes creating a new dataset: 'We set up the first large-scale dataset on personalized image emotion perception, named Image-Emotion-Social-Net, with over 1 million images downloaded from Flickr.' While it mentions obtaining data from Flickr, it does not provide concrete access information (link, DOI, specific repository, or formal citation for public access) for the constructed 'Image-Emotion-Social-Net' dataset itself. |
| Dataset Splits | No | The paper states: 'The first involved 50% images of each viewer in the Image-Emotion-Social-Net dataset are used for training and the rest are used for test.' It explicitly mentions training and testing splits but does not specify a validation set or validation split. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or specific computing infrastructure) used for running experiments were found in the paper. |
| Software Dependencies | No | The paper mentions methods like Naive Bayes and Support Vector Machine but does not provide specific version numbers for any software dependencies, libraries, or frameworks used in the experiments. |
| Experiment Setup | No | The paper discusses evaluation metrics and data splits ('precision, recall and F1-Measure', '50% images... for training and the rest... for test'), but does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, number of epochs) or optimizer settings for their proposed method or baselines. |