Modified GAN with Proposed Feature Set for Text-to-Image Synthesis

Automated synthesis of practical images from the text could be useful and interesting; however, present AI systems are yet far from this objective. Nevertheless, in current years, powerful and generic Recurrent Neural Network (RNN) structures were introduced to train discriminative text feature representation. In the meantime, Deep Convolutional GANs have started producing highly convincing images of specified categories, like room interiors, album covers, and faces. In this research work, we plan to develop a new model for text-to-image synthesis, which contains three important phases: (i) feature extraction, (ii) text encoding, and (iii) optimal image synthesis. Initially, the text features such as improved TF-IDF, bag of words, and N-gram are extracted from the text and they are trained by Bi-LSTM. During the encoding of an image from text, cross-modal feature grouping is performed. Further, the image is synthesized using modified GAN (MGAN) with a new loss function. Here, for precise synthesis of images, the weights of GAN are optimized using Self-improved Social Ski-Driver (SI-SSD) optimization algorithm. Eventually, the superiority of the suggested model is examined via an assessment over existing schemes.