Parametric art creation platform design based on visual delivery and multimedia data fusion

In the era of informational ascendancy, the discourse of artistic communication has transcended the confines of conventional physical domains and geographical boundaries, extending its purview ubiquitously across the global expanse. Consequently, the predominant mode of artistic interaction has evolved towards swift and extensive engagement through virtual platforms. However, this paradigm shift has given rise to the imperative task of meticulous categorization and labeling of an extensive <i>corpus</i> of artistic works, demanding substantial temporal and human resources. This article introduces an innovative bimodal time series classification model (BTSCM) network for the purpose of categorizing and labeling artworks on virtual platforms. Rooted in the foundational principles of visual communication and leveraging multimedia fusion technology, the proposed model proves instrumental in discerning categories within the realm of video content. The BTSCM framework initiates the classification of video data into constituent image and sound elements, employing the conceptual framework of visual communication. Subsequently, feature extraction for both forms of information is achieved through the application of Inflated 3D ConvNet and Mel frequency cepstrum coefficient (MFCC). The synthesis of these extracted features is orchestrated through a fusion of fully convolutional network (FCN), deep Q-network (DQN), and long short-term memory (LSTM), collectively manifesting as the BTSCM network model. This amalgamated network, shaped by the union of fully convolutional network (FCN), DQN, and LSTM, adeptly conducts information processing, culminating in the realization of high-precision video classification. Experimental findings substantiate the efficacy of the BTSCM framework, as evidenced by outstanding classification results across diverse video classification datasets. The classification recognition rate on the self-established art platform exceeds 90%, surpassing benchmarks set by multiple multimodal fusion recognition networks. These commendable outcomes underscore the BTSCM framework's potential significance, providing a theoretical and methodological foundation for the prospective scrutiny and annotation of content within art creation platforms.