Abstract
Sandplay therapy functions as a pivotal tool for psychological projection, where testers construct a scene to mirror their inner world while psychoanalysts scrutinize the testers’ psychological state. In this process, recognizing the theme (i.e., identifying the content and emotional tone) of a sandplay image is a vital step in facilitating higher-level analysis. Unlike traditional visual recognition that focuses solely on the basic information (e.g., category, location, shape, etc.), sandplay theme recognition needs to consider the overall content of the image, then relies on a hierarchical knowledge structure to complete the reasoning process. Nevertheless, the research of sandplay theme recognition is hindered by following challenges: (1) Gathering high-quality and enough sandplay images paired with expert analyses to form a scientific dataset is challenging, due to this task relies on a specialized sandplay environment. (2) Theme is a comprehensive and high-level information, making it difficult to adopt existing works directly in this task. In summary, we have tackled the above challenges from the following aspects: (1) Based on carefully analysis of the challenges (e.g., small-scale dataset and complex information), we present the HIST (HIerarchical Sandplay Theme recognition) model that incorporates external knowledge to emulate the psychoanalysts’ reasoning process. (2) Taking the split theme (a representative and evenly distributed theme) as an example, we proposed a high-quality dataset called \({\textbf {SP}}^2\) (SandPlay SPlit) to evaluate our proposed method. Experimental results demonstrate the superior performance of our algorithm compared to other baselines, and ablation experiments confirm the importance of incorporating external knowledge. We anticipate this work will contribute to the research in sandplay theme recognition. The relevant datasets and codes will be released continuously.
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References
Roesler, C.: Sandplay therapy: an overview of theory, applications and evidence base. Arts Psychother. 64, 84–94 (2019)
Mitchell, R.R., Friedman, H.S.: Sandplay: Past, Present, and Future. Psychology Press (1994)
Lu, D., Weng, Q.: A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 28(5), 823–870 (2007)
Zou, Z., Chen, K., Shi, Z., Guo, Y., Ye, J.: Object detection in 20 years: a survey. In: Proceedings of the IEEE (2023)
Guo, Y., Liu, Y., Georgiou, T., Lew, M.S.: A review of semantic segmentation using deep neural networks. Int. J. Multimed. Inf. Retrieval 7, 87–93 (2018)
Zhao, S., et al.: Affective image content analysis: two decades review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6729–6751 (2021)
Tao, J., Tan, T.: Affective computing: a review. In: Tao, J., Tan, T., Picard, R.W. (eds.) ACII 2005. LNCS, vol. 3784, pp. 981–995. Springer, Heidelberg (2005). https://doi.org/10.1007/11573548_125
Picard, R.W.: Building HAL: computers that sense, recognize, and respond to human emotion. In: Human Vision and Electronic Imaging VI, vol. 4299, pp. 518–523. SPIE (2001)
You, Q., Luo, J., Jin, H., Yang, J.: Robust image sentiment analysis using progressively trained and domain transferred deep networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015)
Jou, B., Chen, T., Pappas, N., Redi, M., Topkara, M., Chang, S.-F.: Visual affect around the world: a large-scale multilingual visual sentiment ontology. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 159–168 (2015)
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Otsuna, H., Ito, K.: Systematic analysis of the visual projection neurons of drosophila melanogaster. I lobula-specific pathways. J. Comp. Neurol. 497(6), 928–958 (2006)
Gamble, K.R.: The Holtzman inkblot technique. Psychol. Bull. 77(3), 172 (1972)
Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehousing Min. (IJDWM) 3(3), 1–13 (2007)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Zhang, Y., Kang, B., Hooi, B., Yan, S., Feng, J.: Deep long-tailed learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell. (2023)
Dosovitskiy, A., et al.: An image is worth \(16 \times 16\) words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Zhang, W., He, X., Lu, W.: Exploring discriminative representations for image emotion recognition with CNNs. IEEE Trans. Multimed. 22(2), 515–523 (2019)
Zhu, X., et al.: Dependency exploitation: a unified CNN-RNN approach for visual emotion recognition. In: IJCAI, pp. 3595–3601 (2017)
Rao, T., Li, X., Min, X.: Learning multi-level deep representations for image emotion classification. Neural Process. Lett. 51, 2043–2061 (2020)
Zhao, S., Jia, Z., Chen, H., Li, L., Ding, G., Keutzer, K.: PDANet: polarity-consistent deep attention network for fine-grained visual emotion regression. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 192–201 (2019)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C., Dosovitskiy, A.: Do vision transformers see like convolutional neural networks? In: Advances in Neural Information Processing Systems, vol. 34, pp. 12116–12128 (2021)
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Feng, X., Hu, S., Chen, X., Huang, K. (2024). A Hierarchical Theme Recognition Model for Sandplay Therapy. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14428. Springer, Singapore. https://doi.org/10.1007/978-981-99-8462-6_20
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