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Footage Transfer Using Convolution Neural Network

1996 words (8 pages) Essay in Information Technology

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Footage Transfer Using Convolution Neural Network

(Classical Machine Learning)

INTRODUCTION

• Footage Transfer with Convolution Neural Network is the process of creating a new image by mixing two image together, one image with actual content and other image with reference image or reference Style and output is other image it’s combination of two images. This is archive by Classical Machine learning is called as Convolution Neural network.
• In this project I Build and Implement the neural network transfer algorithm For the purpose of footage or art generating.
• We already know that drawing an image in art style it’s takes too much time but using neural system we generate image in 30 minutes. We all are acquainted with the network for image classification, which it uses multi-layered convolution network to learn the features required for classification. Multi-layered convolution networks to upgrade the data content of one image to different type. The System uses neural network representation to separate and combine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images.
• Footage transfer is transfer the input image style in to another image style using reference image.
• In machine learning concept, a convolutional neural network is a part of deep, feed-forward artificial neural networks, this is most of time applied to analyzing visual images Content. (wikipedia, 2006)
• The resource for algorithm and trained neural network to getting best results is Tensorflow Mathematical library.
• The Tensorflow implementation is basically slower for high resolution data and its required number of iteration for generating better results.

Concept of Neural reference image Transfer in Footage transfer!!

• The operation on Convolution layer like the style transfer process based on semantic segmentation of the inputs to overcome the content-mismatch problem, which help to improve the accuracy of the results.
• Convolution Neural network train on some data set than this trained network apply to another data set or task is called convolution learning.
• Following the original Neural Style Transfer paper (https://arxiv.org/abs/1508.06576), i’m using version of VGG model for my project like VGG 16 and VGG 19, these network are highly trained on large image data set.
• In this project I implement deep learning approach to footage style transfer it handle a large amount of image content while accurately transfer the reference image. My approach based upon the recent work on neural style transfer by Gatys et al. (L. A. Gatys)
• Footage transfer is the art of generating new content by Combine two image with reference image together. For example two Footage here

INPUT IMAGE

REFERENCE IMAGE

• Output image, it’s based on reference images.

OUTPUT IMAGE

Understanding of Convolution Networks!!

• The great paper on Visualizing and Understanding Convolutional Networks by Matthew D.Zeiler, rog and Zeiler. They have introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. Used in a diagnostic role, these visualizations allow us to find model architectures that outperform on the Image Net classification.
• The final result compress to arbitrary image using Style Matrix is also called as Gram Matrix its collection of set vectors.

• Neural layer Explanation :

Convolution: Applied operation to the input, passing the result to the next layer.

Pooling layer:  combine the outputs of neuron clusters at one layer into a

Single neuron. (wikipedia, n.d.)

• This architecture of VGG-16 contain more than one parameters, and parameters of architecture is easy to learn.one more interesting things about VGG-16 is it’s contain different size of polling and we apply only unique scale for all layer.

This process is applying reputedly for set size of pooling and convolution layer.

• “VGG-16 always uses Convolution Same 3X3XN with stride S=1, the third dimension differs from time to time to increase/decrease the third dimension (N). Also it uses Max Pooling 2×2 stride S=2, pooling layer always have the same third dimension value as input (they play only with width and height)”. (Ramo, 2015).

GOALS & METHODS

• My algorithm takes two images, one is photograph it called input image and second one is style image it called reference image. Here we seek to transfer the style of reference image to input image and by combination of that we generate new image is called artistic image or output image.
• The process of counting changes in each neural layer and data pass through hidden layer to output layer.
• The VGG-19 model of Tensorflow is adopted from VGG Tensorflow with few modifications on the class interface and methods.
• The most of algorithm return the few result or some return type but in Convolution neural network function return gam matrix cost of content image.
• So perform optimization of each function and train neural network for different task.
• Implement the neural transfer algorithm and Generate artistic images using algorithm

Steps for Footage transfer

1. Create an transfer learning
2. Function for input Content.
3. Function for Reference image.
4. Combine all it together to get final image.

-          Pass the result to next level VGG16 model.

-          Construct the default session TensorFlow graph .

-          This Tensorflow graph setup for large image data set in initiate session

And Train a conditional feed forward generator network, where a single

Network is able to generate multiple styles and goes up to 4th iteration.

A. Create a transfer learning.

• We use the 19layer version of VGG model because it’s highly trained on large amount of data set. Using this version to we recognize the lower feature as well as higher feature of network.
• Convolution Neural network train on some data set than this trained network apply to another data set or task is called convolution learning.
• Neural model = Load_vgg_model(pretrain-Neuralmodel/imagenet-vgg-19) than print the Neural model.

In this model all variable store in java dictionary where each variable have key value is tensor variable value. Using this model we have to feed this image to neural model in

TensorFlow we assign value to function like neural model [“input”].assign (inputidata).

Here we assign image to model now next step is run TensorFlow activation function.

• Classical machine learning is highly supported to portable application and different programming languages.
• Convolution neural is called demon process because of convolution neural transfer data format for different programing language.
• Anyway for in this case we are going to use VGG-16 architecture already trained with image net database. VGG-16 is faster than VGG-19, its results too slowly on CPU so we are using VGG-16 instead of VGG-19.
• Public Neural_Model () throws IOException

{

LearningModel learnignmodel= new VGG16();

// creating object of VGG16 model to call function.

Neural_Model vgg16 = (Neural_Model)           learnignmodel.initPretrained(PretrainedType.IMAGENET);

log.info(vgg16.summary());

return vgg16;

}

B) Function for input Content

• As we know convolution neural network identify lower layer as edges and high level layer as more complex for each object of classes.as well as hard to detect text images.
• Here I would like to generated image mean output image is similar content of input image.so we need to choose image layer in middle of the convolution neural network

To get better results.

• Middle layer of convolution layer scale that produce by pixel channel than apply more and more decomposition to get perfect match with content image.
• The reason behind select classical machine learning is pre-trained network are already train on large data scale.
• Here we need some dimensions method for content image and reference image.
• For measuring similarity between two images we use their activation dimensions theory

Proposed by this paper (Leon A.Gatys, 2015)

LContent (

– calculate the cost of data set using first set

d_data = tf.reduce_total((R_H_state – R_K_state)**2) / (4 * v_H * v_W * v_H)

Return d_data

Output

Calculate image cost = 4.38796

Decreasing cost of image, it will help for accurate results.

C) Function for Reference image

• here we have following reference image

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