IMAGE DESCRIPTIVE SUMMARIZATION BY DEEP LEARNING AND ADVANCED LSTM MODEL ARCHITECTURE

Abstract

Auto Image Descriptor is becoming a trending point of interest in current era of research among researchers. Being a great community, which is proposing a continuous and enhanced Iist of intuitive algorithms which is solving to its problems. However, still there are lot of improvement to this field. Therefore, it’s becoming a field of attraction for many researchers and industries and reliable to this digital world. Of these various image descriptive algorithms, some outperform others in terms of basic descriptors requirements like robustness, invisibility, processing cost, etc. In this thesis, we study a new hybrid model image descriptor scheme which when combined with our proposed model algorithm provides us efficient results. Following illustrative points are made to describe the thesis in a nutshell which will later on be discussed in detail.  Firstly, we train our image in 9x9 kernels using CNN model. The idea behind this 1024 kernel of our host image is to divide each pixel of host image with lowest human value system characteristics i.e lowest entropy values and lowest edge entropy values.  Our host image is further divided into 8x8 pixel blocks. Therefore, we’ll have 64 rows and 64 columns are there of the 8x8 blocks. In total 64x64x8 blocks of host image i.e. the size of host image is 512x512.  Now we captionate the pre trained labels to our pixelate model obtained from our CNN model. This will be used in embedding of our labels with the LSTM algorithm. Using vi

LSTM model algorithm, it will assign a label to each pixelate kernel which will perform embedding to the host image.  This embedding and extraction is done Long Short Term Memory algorithm, which is explained in later chapters.  Now using this embedded image, we our Quest Q function of our host image using embed RNN network.  We find best value of Q function using this RNN networks. Also we get the best computed label for our host image using this algorithm.  In a nutshell, this project has combined four major algorithms to generate best results possible. These adopted criteria significantly contributed to establishing a scheme with high robustness against attacks without affecting the visual quality of the image. To describe it briefly the project consists of following four subsections-

1. Convolutional Neural Networks (CNN)

2. Long Short Term Memory Algorithm (RNN)

3. Recurrent Neural Networks (RNN)