Future Image Frame Generation Using Artificial Neural Network with Selected Features

Nishchal K. Verma

Department of Electrical Engineering Indian Institute of Technology

Kanpur, India 208016 nishchal.iitk@gmail.com

Abstract— This paper presents a novel approach for the generation of Future image frames using Artificial Neural Network (ANN) on spatiotemporal framework. The input to this network are hyper-dimensional color and spatiotemporal features of every pixel of an image in an image sequence. Principal Component Analysis, Mutual Information, Interaction Information and Bhattacharyya Distance measure based feature selection techniques have been used to reduce the dimensionality of the feature set. The pixel values of an image frame are predicted using a simple ANN back propagation algorithm. The ANN network is trained for R, G and B values for each and every pixel in an image frame. The resulting model is successfully applied on an image sequence of a landing fighter plane. As Mentioned above four feature selection techniques are used to compare the performance of the proposed ANN model. The quality of the generated future image frames is assessed using, Canny edge detection based Image Comparison Metric(CIM) and Mean Structural Similarity Index Measure(MSSIM) image quality measures. The proposed approach is found to have generated six future image frames successfully with acceptable quality of images.

Keywords- Future Image Frame generation, Spatiotemporal framework, Feature selection, Artificial Neural Network.

I. INTRODUCTION Prediction of future data from its past samples has always

been an area of great interest. This paper proposes an efficient method to predict future image frames of an image sequence. Future image frames of the image sequence of an event using the past information of the image sequence. To generate an image frame, the R, G and B components of each and every pixel of it has to be generated. This has motivated to model an image sequence in such a manner that it can capture the color components associated with the space as well as the time component Many earlier attempts have successfully made use of the spatiotemporal representation for an image sequence [1],[2]. The challenges in the generation of future images are to utilize the spatiotemporal representation along with a suitable ANN modeling scheme with proper feature selection techniques. Some preliminary work has already been done in this direction[2],[3],[4] using additive GFM [5]. In an image sequence modeling, we often encounter with poor representation of objects causing many uncertainties and contains irrelevant data that unnecessarily increases the dimension of the input features. Research literatures suggest [2],[6] that ANN theory along with feature selection could be useful in dealing with computational complexities involved in

future frame generation. For future image sequence modeling initiatives, ANN modeling approach claim for their strong candidature. Utilizing these concepts and related formulations motivated to develop a useful ANN modeling scheme along with feature selection for generation of landing fighter plane images. In this framework, space and time are treated uniformly in the spatiotemporal domain called unified feature space[1]. The extracted space-time regions allow us for the generation of spatiotemporal events. In this paper, images of a fighter aircraft landing on an aircraft carrier are used for the implementation of the model. The ANN model can generate, with a good accuracy, future image frames of the fighter planes. Also to evaluate the quality of the generated future image frames, the structural similarity of corresponding actual image frame is computed using CIM and MSSIM indices. This work may also be helpful in early detection and target tracking and to take defense measures in advance. The rest of the paper is organized as follows: In Section-II, the feature extraction and spatiotemporal representation of image sequence is discussed. Feature selection techniques are discussed in Section-III. Section-IV explains the formulation of the ANN model for image frames generation. The step-wise algorithm is given in Section-V. In Section-VI, the assessment of the structural quality of generated future satellite image is discussed. Next, actual images of the fighter, are used for validation of the system in Section-VII. Finally, the conclusions are drawn in Section-VIII.

II. SPATIOTEMPORAL DOMAIN AND FEATURE EXTRACTION

For the generation of future image frames, both spatial and temporal information are needed to be considered in an integrated manner. An image sequence contains consistent visual contents that change over time. The image sequence is to be seen as a single entity, as opposed to a sequence of separate image frames. Space and time need to be treated uniformly. An image sequence has both spatial and temporal features. Feature selection reduces the amount of data to be processed and helps in getting better output as only relevant information to be used. Of the various schemes [7] available in the literatures, the spatiotemporal feature space, i.e., representation of an image sequence in spatiotemporal domain is found to be more suitable for the problem at hand. For RGB color scheme features like color components R, G and B of every pixel and their location coordinate in x and y along with the time of occurrence of the frame at tth instant, in the image sequence are extracted and a six dimensional unified feature space, (R, G, B,

x, y, t) is used in [1],[2],[3],[4],[5] for representation of spatiotemporal images and in this way all the pixels in each frame of an image sequence are uniquely defined.

III. FEATURE SELECTION Feature selection is normally known as variable selection,

feature reduction, attribute selection or variable subset selection, Feature selection is a process of selecting a subset of relevant features for building robust learning models. Large number of features increase computational complexities. By removing irrelevant and redundant features from the data,

feature selection process helps in improving the performance of models. Enhancing generalization capability, speeding up learning process and improving model ability to interpret. Most of the time it is not possible to identify usefulness of features by intuition. Hence, the need arises for an automatic feature selection process to select optimal feature set. While developing models for classification, the features form a feature space where they are also called as variables or dimensions of feature space. Optimal feature set contains all relevant features and ignores irrelevant and redundant features. However, no single algorithm guarantees this in general to generate an optimal set of features. Selection process is very much dependent to a specific problem in hand. There are many feature selection techniques available depending upon the feature space and size of the feature set. Pixels in an image are highly correlated to the pixels in their neighborhood. In this paper Principal Component Analysis(PCA)[8], the Bhattacharyya Distance measure [9],[12],[13],[14] (derived from Bhattacharyya coefficient [10]) and the Interaction Information[11],[15],[16] are used to extract relevant features.

IV. ANN MODEL FOR FUTURE IMAGE FRAME GENERATION

In this section, a simple separate ANN models for future image frame generation are derived using R,G and B color components of each pixel with the assumption of independence of features or attributes. A Simple architecture of ANN Model for R-component with n-inputs is shown in Figure 1, having one hidden layer with m neurons and one output layer neuron. The training of ANN is mainly undertaken using the Back-propagation learning algorithm [6], [17]. Back-propagation algorithm is one of the training algorithms for feed-forward

neural networks,

which uses two parameters in conjunction with the gradient descent learning algorithm. The first parameter is the learning rate η , which is essentially a parameter that determines how directly the gradient descent should be applied to the weight matrix and threshold values. The gradient is multiplied by the η and then added to the weight matrix or threshold value. This will slowly optimize the weights to values that will produce a lower error. The second parameter is called momentum, which specifies, to what degree, the weight changes from the previous iteration should be applied to the current iteration. This helps back propagation algorithm to come out of local minima.

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x 5)

V. ALGORITHM FOR FUTURE IMAGE FRAME GENERATION USING ANN

A. The Algorithm

Assuming that we have L no. of image frames in an image sequence. To derive the suitable ANN model, the image sequence is split into two sets: the set of first F frames be

called rT (training set) and the set of remaining L - F frames be sT (the testing set). Use set rT to develop the ANN model and for its training. Use this model to generate the future frames. Use the set sT to validate the generated frames. It is important to note that for real time application, test set may not be used and whole image sequence data may be used for incremental training of ANN. Step 1: Perform feature extraction on the image sequence and make a unified feature space representation in spatiotemporal domain, i.e. a six-dimensional feature space (R G B x y t) as discussed in section II. This means representing each pixel of

an image in the sequence with a six dimensional feature vector [R G B x y t]. Step 2: Arrange the four-dimensional feature vectors for all color space as: [R, x, y, t], [G, x, y, t] and [B, x, y, t] for each pixel from the six-dimensional feature space. These feature spaces along with neighboring and past pixel values will serve as the final

feature set for the corresponding ANN model. Step 3: Employ an appropriate feature selection technique, to reduce the dimensionality of the feature set, for each color component R, G and B. Step 4: Based on the space and time inputs estimate R, G and B values separately using a model suitable ANN models ANN-R, ANN-G and ANN-B as shown in Figure 1. Step 5: Aggregate R, G and B components of corresponding image pixels to construct the future image frames. Step 6: Apply Canny edge detection algorithm on the test and the generated future image frames for structural comparison based on CIM and MSSIM indices.

Basic Feature Extraction( Pixel

representation in 4-D Spatiotemporal domain

(R/G/B,x,y,t) in an unified feature space

4-Dimensional feature data i.e. R- Component, Spatial(x,y) and

temporal alongwith neighboring and past pixel data

4-Dimensional feature data i.e. G- Component, Spatial(x,y) and

temporal alongwith neighboring and past pixel data

4-Dimensional feature data i.e. B- Component, Spatial(x,y) and

temporal alongwith neighboring and past pixel data

Feature Selection using suitable

algorithm

Feature Selection using

suitable algorithm

Feature Selection using

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ANN-Model for R-Component

ANN-Model for G-Component

ANN-Model for B-Component

Aggregation of R,G and B

components for every pixel

Future Generated Image Frames

Image

Sequence (A set of image frames)

Fig. 2: Generation of Future images using ANN model along with Feature selection components

B. Implementation

The image sequence(a video clip in general) for which image frames are to be generated is first converted into a set of its constituent image frames. This set is divided into a training and a testing set. The training set image frames are then used to learn to ANN color model Since separate ANN models have to be created for each color component. A four dimensional vectors [R, x, y, t], [G, x, y, t] and [B, x, y, t] from the six dimensional feature space is extracted along with neighboring and past pixel values to train ANN-R, ANN-G and ANN-B. For every pixel separate ANN model is developed for all three color components. For example, if the frames of the image sequence are of size 100 x 100 then the frame to be generated will be of the same size hence we will be having total 10000 x 3 ANN models, one for each pixel for each color component. The pixels in an image frame are strongly correlated to their neighboring pixels in terms of the variation of the color component. This correlation can be used in model as the network can be trained for this variation to increase its prediction capability. These neighboring pixels along with the required one act as feature set to the generation model. To select the best pixel values among all the neighboring ones feature selection is performed on this feature set. After selecting the best set of features, the network is developed depending upon the number of input features, the output and the parameters like number of hidden layer and hidden layer neurons. Each of these ANN networks is trained separately for the variation in the corresponding color for respective pixel over all training set frames. The color component R, G and B of a pixel of future image frame is then generated based on the variations of last two frames of the training set. These generated color components are then aggregated for each pixel to generate the final future image.

VI. IMAGE QUALITY ASSESSMENT Most of the image similarity assessment metrics like peak signal to noise ratio (PSNR), picture quality scale (PQS), noise quality measure (NQM), mean structural similarity (MSSIM), information fidelity criterion (IFC), Canny based image comparison metric (CIM) and visual information fidelity (VIF) perform well on gray scale images. Out of these seven indices, two indices, MSSIM [18] and CIM [19] have been chosen for the evaluation of structural similarities in color images in our image generation applications. The reason for choosing CIM and MSSIM based metric is that CIM index is invariant to rotation, scaling and translation within a reasonable tolerance and MSSIM has been stated to be a better structural descriptor[18] for images. The indices are computed for each color component, R-image, G-image and B-image and then averaged to get a composite index for color RGB images.

VII. SIMULATION AND RESULTS For experimental validation, image sequence of a fighter plane landing on an aircraft carrier is under taken where there is camera motion and zooming effect involved. This special case is considered for simulation as this involves some camera motion(being on a ship) and zooming effect(fighter

approaching the camera). The camera motion causes the background to change and zooming effect will cause the regular change in the object size. This means the network has to be tuned for both motion and size change of the object as well as the changing background. The training set consists of 120 images of size 153 x 116 pixels and the testing set has 6 images of similar size. Note that, the training images and the test images correspond to two non-intersecting time interval. In other words, the test images are the future images that is not seen by the model estimation process. The results constitutes the simulation of the algorithm for four different categories in which one is by using only ANN and the rest three involves ANN with three feature selection techniques Principal Component Analysis (ANN-PCA), Interaction Information(ANN-II) and Bhattacharya Distance(ANNBD). The inputs correspond to a window of two subsequent frames occurring at t - 2 and t -1 and are centered at pixel located at ( ), .x y Generation of images without feature selection trains the ANN using back-propagation with ten input variables:

( )C x, y -1, t - 2 , ( )C x -1, y, t - 2 , ( )C x, y, t - 2 ,

( )C x +1, y, t - 2 , ( )C x, y +1, t - 2 , ( )C x, y -1, t -1 ,

( )C x -1, y, t -1 , ( )C x, y, t -1 , ( )C x +1, y, t -1 , ( )C x, y +1, t -1

and one output variable i.e. ( )C x, y, t . The window covers the immediate vertical and horizontal neighborhood of the pixel at ( ), .x y In case of ANN with feature selection the input to the ANN are chosen from a set of 18 features

( )C x -1, y +1, t - 2 , ( )C x, y -1, t - 2 , ( )C x -1, y, t - 2 ,

( )C x +1, y -1, t - 2 , ( )C x, y, t - 2 , ( )C x -1, y +1, t - 2 ,

( )C x +1, y +1, t - 2 , ( )C x +1, y, t - 2 , ( )C x, y +1, t - 2 ,

( )C x, y -1, t -1 , ( )C x -1, y, t -1 , ( )C x -1, y -1, t - 2 ,

( )C x +1, y -1, t -1 , ( )C x, y, t -1 , ( )C x -1, y +1, t -1 ,

( )C x +1, y, t -1 , ( )C x, y +1, t -1 , ( )C x +1, y +1, t -1 and one

output variable ( )C x, y, t . This window covers the immediate vertical, horizontal and diagonal neighborhood of the pixel at ( ), .x y Out of these 18 features, we select 10 best features, ranked according to the maximum correlation with the output class, using feature selection techniques. ( )C x, y, t represents

the color values R, G and B of the pixel, located at ( ),x y in the frame occurring at time t, for ANN-R, ANN-G and ANN-B respectively. This is to be noted that color information of the previous time instants and spaces are included in the input vector to capture the dynamics as well as to enhance the performance of the system. The images are generated for 1, 3 and 10 hidden layer neurons for different η =0.01,0.05 and 0.09 in each case. The ANN network used has ten input neurons (in the input neuron layer) and one output neuron (in the output neuron layer) with one hidden layer. The activation function of hidden layers is unipolar sigmoid and output layer

is ‘pure-linear’. The momentum is kept Figure 3 shows the actual image and tgenerated image by using ANN-PCA witneurons. It was found from the simulatigenerative methods has best CIM and Mgenerative images for 3 hidden layer neurtables I, II, III, IV, V, VI, VII and VIII. Ittables that CIM and MSSIM indices are highwith η = 0.05.

(a) Test Image 121

(d) Generated Image 121

(a) Test Image 124

(d) Generated Image 124

Fig. 3: Test Images(actua

constant at 0.56. the corresponding th 3 hidden layer ons that all four

MSSIM indices of rons as shown in t is clear from the hest for ANN-PCA

Figure 4 shows the comparisoshows the comparison of MSSby all four generative methods=0.05 and α =0.56. It is clear fmethod is efficient compared tof quality of the generated futu

(b) Test Image 122

(e) Generated Image 122

(b) Test Image 125

(e) Generated Image 125

al images) and Generated Images using ANN-PCA with 3 hidden lay

on of CIM index and figure 5 SIM index of images generated s with 3 hidden layer neuron, η from the graphs that ANN-PCA to other three methods in terms

ure image frames.

yer neurons

(c) Test Image 123

(f) Generated Image 123

(c) Test Image

(f) Generated Image 126

Fig. 4: CIM Inde

Fig. 5: MSSIM Index for 3 hidden layer neurons α = 0.5

ex for 3 hidden layer neurons α=0.56 and η=0.05

56 and η = 0.05

TABLE I: CIM Indices for ANN with 3 hidden layer neuron

Image no.

Learning Rates

0.01 0.05 0.09

121 0.9868 0.9881 0.9829

122 0.9779 0.9878 0.9793

123 0.9772 0.9786 0.9766

124 0.9764 0.9772 0.9743

125 0.9734 0.9745 0.9743

126 0.9677 0.9696 0.9675

TABLE II: CIM Indices for ANN-PCA with 3 hidden layer neurons

Image no.

Learning Rates

0.01 0.05 0.09

121 0.9883 0.9913 0.9859

122 0.9793 0.9888 0.9799

123 0.9776 0.9856 0.9769

124 0.9767 0.9786 0.9763

125 0.9743 0.9751 0.9747

126 0.9678 0.9716 0.9678

TABLE IV: CIM Indices for ANN-BD with 3 hidden layer neurons

Image no.

Learning Rates

0.01 0.05 0.09

121 0.9819 0.9889 0.9817

122 0.9817 0.9850 0.9810

123 0.9779 0.9845 0.9773

124 0.9734 0.9769 0.9704

125 0.9718 0.9742 0.9685

126 0.9582 0.9709 0.9592

TABLE VI: MSSIM Indices for ANN-PCA with 3 hidden layer neurons

Image no.

Learning Rates

0.01 0.05 0.09

121 0.9871 0.9892 0.9810

122 0.9647 0.9663 0.9627

123 0.9342 0.9430 0.9277

124 0.9230 0.9244 0.9149

125 0.8797 0.8869 0.8751

126 0.8437 0.8495 0.8398

TABLE V: MSSIM Indices for ANN with 3 hidden layer neurons

Image no.

Learning Rates

0.01 0.05 0.09

121 0.9853 0.9854 0.9825

122 0.9640 0.9642 0.9604

123 0.9334 0.9355 0.9316

124 0.9224 0.9247 0.9204

125 0.8745 0.8821 0.8799

126 0.8432 0.8457 0.8368

TABLE III: CIM Indices for ANN-II with 3 hidden layer neurons

Image no.

Learning Rates

0.01 0.05 0.09

121 0.9843 0.9856 0.9825

122 0.9714 0.9786 0.9744

123 0.9673 0.9678 0.9635

124 0.9570 0.9632 0.9597

125 0.9563 0.9602 0.9528

126 0.9551 0.9572 0.9510

TABLE VII: MSSIM Indices for ANN-II with 3 hidden layer neurons

Image no.

Learning Rates

0.01 0.05 0.09

121 0.9824 0.9830 0.9817

122 0.9600 0.9613 0.9593

123 0.9289 0.9312 0.9263

124 0.9165 0.9181 0.9145

125 0.8713 0.8718 0.8707

126 0.8370 0.8408 0.8364

TABLE VIII: MSSIM Indices for ANN-BD with 3 hidden layer neurons

Image no.

Learning Rates

0.01 0.05 0.09

121 0.9827 0.9832 0.9821

122 0.9640 0.9643 0.9613

123 0.9323 0.9330 0.9234

124 0.9212 0.9214 0.9124

125 0.8741 0.8769 0.8405

126 0.8430 0.8450 0.8298

VIII. CONCLUSION The performance of the proposed algorithm for an image set of a fighter plane landing on a ship has been studies. Six future images of the image sequence has successfully been generated capturing the motion of the plane successfully. Four variations of the image generation algorithm has been simulated and are analyzed on the basis of quality (CIM and MSSIM indices) of generated image frames. Feature selection methods has been employed to select the optimum features for future image frame generation. It has been deduced that feature selection helps in improving the efficiency of future

image frame generation model. Also from the simulation it is clear that ANN-PCA model with 3 hidden layer neuron for η= 0.05 and α = 0.56 is the optimum future image generation scheme.

ACKNOWLEDGEMENT Author sincerely thanks Department of Science and Technology (DST), New Delhi, India for providing financial support under project no DST/EE/20100272 to carry out this research work.

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