High-level and Low-level Feature Set for Image Caption Generation with Optimized Convolutional Neural Network

Automatic creation of image descriptions, i.e. captioning images, is an important topic in artificial intelligence (AI) that bridges the gap between computer vision (CV) and natural language processing (NLP). Currently, neural networks are becoming increasingly popular images researchers looking for more efficient models CV sequence-sequence systems. This study focuses on a new caption generation model divided into two stages. Initially, low-level features, such as contrast, sharpness, color their high-level counterparts, motion facial impact score, extracted. Then, optimized convolutional network (CNN) harnessed to generate captions from images. To enhance accuracy process, weights CNN optimally tuned via spider monkey optimization with sine chaotic map evaluation (SMO-SCME). The development proposed method evaluated diversity metrics.