DoppelSearch: A Novel Approach to Content-Based Video Retrieval for AI Challenge HCMC 2023

Video retrieval, which has been considered as a critical task in the field of computer vision and pattern recognition recently, finds extensive applications in several aspects such as education, entertainment, security, and healthcare. However, it faces challenges due to the complexity of video data, the instability in feature extraction methods, or the semantic disparities between videos and text. In this paper, we present a novel approach for content-based video retrieval, named DoppelSearch, leveraging the CLIP (Contrastive Language-Image Pre-training) model architecture to classify and label video segments, offering users the ability to search for videos based on specific content. Our method capitalizes on the ViT-b/32 model for feature extraction and employs feature embedding, in conjunction with the Faiss library, to enhance search efficiency. Experimental results demonstrate our model’s high accuracy and swift retrieval times, promising new opportunities in content-based video retrieval for researchers, developers, and end-users. This paper not only introduces the application of the CLIP and ViT-b/32 models, but also elaborates on the specific feature extraction process and the utilization of Faiss for optimizing video retrieval. The DoppelSearch method represents a significant stride in the field of video retrieval and holds promise for diverse applications across various industries.