Video Captioning And Emotion Recognition Using Cnn+lstm

Abstract

With The Rapid Growth Of Digital Video Content, Particularly Across Social Media Platforms, Short And Engaging Videos Have Become Increasingly Dominant In Capturing User Attention. Video Captioning Plays A Critical Role In Addressing This Trend By Automatically Generating Descriptive Textual Representations Of Video Content, Thereby Improving Accessibility And Enhancing User Engagement. The Process Of Video Captioning Involves Two Primary Stages: Feature Extraction And Caption Generation. In This Work, Pre-trained Convolutional Neural Networks (CNNs), Such As InceptionV3 And VGG16, Are Employed To Extract High-level Visual Features From Video Frames. These Extracted Features Are Subsequently Provided As Input To A Long Short-Term Memory (LSTM) Network, Which Generates Contextually Coherent Captions.The Incorporation Of LSTM Networks In Conjunction With Word Embeddings Facilitates The Generation Of Semantically Meaningful Captions While Enabling Effective Emotion Classification. This Integrated Framework Significantly Enhances The Overall Understanding Of Video Content. Overall, This Work Presents A Comprehensive And Efficient Solution For Intelligent Video Interpretation By Integrating Visual Feature Extraction With Contextual And Emotional Analysis, Thereby Advancing The Capabilities Of Automated Multimedia Understanding Systems.

Keywords