With the development of comfortable and user-friendly devices for capturing and storing multimedia content, a huge amount of videos are being shot at every moment. Nearly 60 hours worth of footage is uploaded on YouTube in every minute
. To find and analyze this huge amount of videos have become an extremely tedious task. The generation of a compact, comprehensive, and automated summary of video can facilitate an effective way to utilize videos for various real-life applications such as for classifying huge number of online videos, removing redundant videos, highlighting the sports matches or trailer of feature films. Also, a semantical relevant position can be located using video summaries that can be essential for surveillance system. Fig. 1 shows an illustrative example of summary of a video titled “Reuben Sandwich with Corned Beef & Sauerkraut” available in YouTube. A number of frames of the video are grouped together based on the noticeable contexts as a summary of the video. It is evident from Fig. 1 that a context-dependent video summary can have a better representation as compared to uniform sampling of video frames.
1.1 Related Works
In general, there are three major approaches to summarize videos . They are: object-based, event-based and feature-based methods. Object-based approach mainly depends on detecting the highlighted objects. The underlying assumption is that these objects are the key elements for a summary. In other words, the frames in which these objects are found can be considered as the important frames to be presented in the summary . Lee et al.  used these object-based detection of key frames to summarize egocentric videos. Though this approach is effective for certain types of videos, the success of the methods largely depends on the content of the videos. If a highlighted object is not present throughout the entire video or the highlighted object is present in every frame of a video, then object-based detection methods will not be able to summarize the video effectively.
In the event-based methods, an important event is determined by the use of previously defined bag-of-words. The events can be detected by the change in various low-level factors, e.g., change in colors or abrupt change in camera direction. These methods are used by many works in the literature, when the goal as well as the environments of summarization is very much specific, e.g., surveillance videos , sports videos , coastal area videos . This approach fails to represent the overall generality as similar events can have contrasting significance in different environments. For example, in a video of a football match, the scene of scoring goal is considered important, but similar event in a surveillance video can be useless.
Most popular methods for video summarization are based on suitable features. In this approach, certain features are used to detect important frames termed as key frames. In most cases, a large number of features are combined together to detect important frames. These features are selected by judging the content of the videos. Different types of features including the visual attention 
and singular value decomposition (SVD)
have been used for key frame detection. Recently, machine learning techniques have been introduced to select suitable features. However, the success of such methods seriously depends on the number of selected features, and the way the features are combined. Hence, the methods fail to map individual perception in a generalized framework.
1.2 Scope of Work
Most of the existing methods for video summarization focus on detecting key frames based on some sorts of fixed parameters. This type of parameter-based detection is not suitable for an overall general platform of video summarization. In this work, a convolutional neural network (CNN)-based architecture is proposed to deal with the overall generality of the problem and to estimate the importance of each frame in a video. This can be used to develop a platform in which a user can have freedom to select the length of the summary as applicable. To the best of our knowledge, finding out the frame-level shot importance using the CNN is not present in the current literature.
1.3 Specific Contributions
The main objective of the paper is to present a CNN model to estimate the shot-by-shot importance in a video. The overall contributions of the paper are:
Developing a CNN based algorithm to estimate frame-level shot importance.
Generating a platform for the summarization of any kind of video using the estimated frame-level shot importance.
The rest of the paper is organized as follows. Section 2 provides a description of the proposed architecture. The experimental setup and the results obtained are described in Section 3. Finally, Section 4 provides the conclusion.
2 Proposed Method
In this paper, a feed-forward CNN is employed to determine the frame-level shot importance of a video. In the proposed multilayer CNN, the first layer is the input layer which uses the raw video frame as the input. The last layer is the output layer that predicts the importance score of the input frame in that particular video, where is a positive integer corresponding to the highest score. A low value of indicates a less important frame, while a high value implies an important one. In this section, first the proposed CNN model is described. Then, the training and optimization schemes are detailed.
2.1 CNN Model
In order to estimate the shot importance of a frame, we train an end-to-end CNN that automatically learns visual contexts to predict the score in the output. The proposed CNN architecture is a six-stage model employing learnable convolution and fully connected layers as shown in the stick diagram in Fig. 2
. The convolution and fully connected operations are followed by ReLU activation for its ability to help neural networks attaining a better sparse representation. The first stage of the network is pre-processing tasks needed to normalize the dimension of the data. The pre-processing stage can be written as
This task involves frame resizing and cropping that are applied sequentially. In the stick diagram of Fig. 2 the frame resizing is shown using a rectangle with a single stripe and cropping operation is shown by a diverging trapezoid. The second stage performs a convolution operation which employs ReLU activation on , which is given by
where is the convolution operation, is the ReLU operation. In Fig. 2
, the convolution layer is shown as a rectangle and ReLU layer as a solid line. The third and fourth stages use the convolution, ReLU and max-pooling operations serially. These operations are given by the equations
where is the max-pool operation. This operation reduces the spatial dimension by half and is represented by converging trapezoid (see Fig. 2). The fifth stage consists of fully connected operation, the ReLU and dropout layers. First, the output of fourth stage
is flattened to a 1-D vector, and then this vector is fed into the fifth stage to provide the output given by
where is the dropout operation . In Fig. 2, the fully connected layer and the dropout layer are represented by rectangle with three stripes in the middle and a parallelogram, respectively. The final part of the CNN is the regressor which is a fully connected layer that outputs the estimation of the frame importance in scalars from given by
In many cases, the frame-level importance can be averaged over a few neighboring frames using a smoothing filter (shown as rectangle with a diagonal stripe in Fig. 2). Overall, the learnable parameters of the network are the filter sets, , , , , and , and their corresponding bias terms , , , and , respectively.
2.2 Training and Optimization
There are number of training and optimization schemes that can be chosen for attaining good results from the network. An effective choice of initialization of weights and biases can significantly reduce training time by converging the network faster. In this context, we have explored the works of Glorot et al.  and initialized all the biases with zeros and weights
at each layer by taking samples from a uniform distributionwhere is the size of the previous layer. In order to apply back-propagation 
for training the network, a loss function is required to be specified that is easily differentiable. For regression-based tasks such as the estimation of scores, most common choices are-norm, -norm or Frobenius norm. In the proposed method, we choose an -norm-based loss function given by
where is the ground truth value of the shot importance, is the predicted score, and is the number of training inputs fed into the back-propagation process in each iteration for mini-batch optimization . During the training period, this function is optimized by using the contemporary Adam stochastic optimization technique . The weights of the filter sets denoted by are updated based on the first moment and second moment of the gradient of the loss function with respect to the weights. Overall, the update process of the optimization can be written as 
where is the step size, and are decay rates for the first and second moments, respectively, and is a factor of numerical stability.
3 Experiments and Results
Experiments are carried out to evaluate the performance of the proposed CNN architecture as compared to existing methods for predicting the score of frame importance in videos. In this section, first we give an overview of video dataset used in the experiments, then we describe our training and testing data partitions, data augmentation techniques, parameter settings of the proposed architecture, and matching scheme of estimated score of importance with the ground truth. Then, the methods compared for performance evaluation are introduced. Finally, results are presented and evaluated in terms of commonly-referred performance metrics of regression.
In the experiments, we have used the TVSum50 database  that includes 50 video sequences. These videos are categorized into ten different genres including the flash mob, news, and video blog. Each genre contains videos of five independent scene. The duration of videos varies from 2 to 10 minutes. Each frame of these videos has been annotated by an importance score of continuous values ranging from 1 to 5 by using crowd-sourcing. It is found empirically that a shot length of two seconds will be able to the reflect local context of a video . By adopting this rule, each video is divided into segments, where each segment has a duration of two seconds. These segments are first annotated by 20 users. A ground truth of importance score has been produced by regularizing and combining these annotated scores.
Out of 50 videos of the dataset, 35 videos are chosen for training and the mutually exclusive rest of the 15 videos are kept for testing phase. In order to design a fair evaluation process, at least three videos for the training set and one video for testing set are included from each of the ten genres. In order to achieve a computational efficiency and to reduce the training period, a subset of frames from videos are considered for learning. In particular, a single frame from each strip of five consecutive frames is considered for training scheme. This is mainly due to the fact that the visual contents of five consecutive frames are almost same in a video. This ensures that the training data has less amount of redundant information and, thus the approach significantly reduces the training period. On the other hand, no frames is discarded from the test set, instead the importance score of every frame of a video is predicted.
3.3 Data Augmentation
Data augmentation helps to achieve generalized results in CNN-based learning . It reduces overfitting by virtually increasing the training data size. In general, a larger network can be trained by augmenting a dataset without losing validation accuracy. This scheme has been adopted in our experiments. Augmentation techniques that are used in the training include the transpose, horizontal flips, and vertical flips of the frames. One or more of these operations are chosen randomly in each stage of the training step. In other words, seven new variants of the original data are achieved, and our training set virtually increases by up to 8 times. During each iteration, a random integer is generated between 1 and 8 inclusive that correspond to a specific combination of data augmentation techniques. Based on the generated integer, the selected operations are performed on the data prior to feeding it to the following stage.
3.4 Parameter Settings
The network parameters of the CNN model described in Section 2.1 are chosen based on the dimensions of input and required output in different layers. Since the size of input video frames varies among different videos, first the video frames are resized to and then cropped centrally to obtain sized images, where is the channel parameter of RGB components of a color image. The number of filters in the sets , , , , and and corresponding number of bias terms , , , and are set to , , , and , respectively, since such a choice provides an overall good performance. The kernel size of all the convolution filters is set to and that of the max-pool operation is set to . The dropout parameter is chosen as during training and during testing. Empirically the parameters , , of the Adam optimizer are found to be , and , respectively. The numerical stability factor is set to .
3.5 Matching of Importance
A single value has been assigned as the shot importance for 50 neighboring frames in the ground truth. Since the proposed model predicts shot importance for each of the frames in a video, a scheme for matching the importance has been employed in order to be consistent with the ground truth of dataset. In particular, first the predicted output values for 50 consecutive frames are considered, then the minimum and maximum of the predictions are discarded, and finally the root mean squared (RMS) value of the remaining data is assigned as the fixed-level shot importance for the 50 neighboring frames.
3.6 Comparing Methods
The proposed CNN is a learning-based method, where the importance of frames are predicted automatically by the network. In the experiments, we select three feature-based approaches reported for video summarization. Originally the methods are concerned with the selection of key frames. The methods are briefly described as follows:
Visual attention : In this method, the visual attention extracted from spatial and temporal saliency is used to extract key frames from a video.
Singular value decomposition (SVD) : The minimization of cross correlation of the features extracted in terms of SVD of frames is used to identify the key frames for video summarization.
To compare these methods with the proposed one, they are invoked to predict shot importance for each of the frames of a video. In particular, the features are used in a support vector regression technique to predict the frame-level shot importance using the same training and testing sets described in Section 3.2.
3.7 Evaluation Metrics
The performance of the proposed CNN-based method and three comparing methods are evaluated in terms of three metrics, namely, mean absolute error (MAE), absolute error variance (AEV), and relative -measure. The MAE indicates how much the predicted values deviate from the ground truth on average, and the AEV reveals the fluctuations of absolute errors. Thus, a lower value of MAE means predicted value is very close to the actual one. Similarly, a small AEV is a good sign implying that errors do not fluctuate significantly.
The -measure gives an idea about the close matching between the video summary prepared by the predicted shot importance and that by the ground truth. In order to compute the -measure, a threshold is selected for each of the comparing methods as well as for the ground truth. The threshold maps the continuous values of frame importance into binary values denoting the selected and non-selected frames for a summary, preferably with a length of of the original video. The metric -measure is given by
where is the fraction of matched frames with respect to the ground truth, and implies the fraction of matched frames with respect to the total number of frames. To find out how well the proposed CNN-based method performs as compared to others, the relative -measure is evaluated by normalizing the metric with the same calculated from the annotated ground truths of fifteen videos.
In the experiments, shot importance of all the frames of test videos are predicted using the proposed as well as three comparing methods. Then, the importance values are grouped for local neighboring 50 frames as described in Section 3.5. Table I shows the overall prediction performance of the testing videos in terms of the metrics MAE, AEV and relative -measure. It is seen from the table that the proposed CNN-based method performs the best by providing the lowest MAE. It shows approximately improvement in terms of MAE from the most competitive method reported in , which uses SVD of frames as features. The proposed method outperforms the comparing methods by showing an improvement of at least in robustness by providing the lowest AEV. It can also be found from Table I that our method provides the highest relative -measure as compared to others, where the improvement is more than from the competing method. In other words, our proposed method performs significantly better than others for predicting the shot importance. This is evident because the method consistently provides low absolute errors through out the entire frames of a video and thus results in a video summarization close to the ground truth.
Fig. 3 shows the frame-level scores of shot importance predicted for first two thousand frames of a test video with flash mob genre having a title of “ICC World Twenty20 Bangladesh 2014 Flash Mob Pabna University of Science & Technology (PUST)”. This video was shot by a group of Bangladeshi students as a promotional video of the 2014 ICC World Twenty20 event. It is seen from Fig. 3 that the predicted scores of importance provided by the proposed CNN-based method tend to follow the ground truth more closely than that provided by the comparing three methods. The motion-based method  shows sudden changes of scores of importance, which appear even in the opposite direction to the trend of the ground truth. The visual attention  and SVD-based  methods though follow the trend of ground truth closely in a few region, the deviations are significant in most of the regions. Evidently, the above two limitations are nearly absent in the prediction scores of the proposed method, and hence, the CNN-based prediction appears to be accurate and robust.
In this paper, a CNN-based architecture has been proposed to predict frame-level shot importance of videos. The predicted scores of shot importance can be used for the development of a platform, which can provide a user-oriented automated summary of a video. Thus, our work successfully converts the subjective video summarization into a measurable objective framework. To evaluate the proposed CNN-based method, annotated importance of ten genres of videos of TVSum50 database have been used as the ground truth. Experiments have been conducted by adopting mutually exclusive training and testing sets that encompasses available genres of the dataset. The proposed method has been compared with the methods based on the visual attention, motion attention, and SVD features. Experimental results reveal that the proposed CNN-based method outperforms the existing feature-based methods in terms of three evaluation metrics, namely, MAE, AEV and relative-measure.
-  S. Brain, “YouTube statistics,” Retrieved from statistic brain: http://www.statisticbrain.com/youtube-statistics, 2014.
-  A. G. Money and H. Agius, “Video summarisation: A conceptual framework and survey of the state of the art,” J. Visual Communication and Image Representation, vol. 19, no. 2, pp. 121–143, 2008.
-  W. Ding and G. Marchionini, “A study on video browsing strategies,” University of Maryland at College Park, College Park, MD, Tech. Rep. Report No. UMIACS-TR-97-40, 1998.
-  J. Meng, H. Wang, J. Yuan, and Y.-P. Tan, “From keyframes to key objects: Video summarization by representative object proposal selection,” in
-  Y. J. Lee, J. Ghosh, and K. Grauman, “Discovering important people and objects for egocentric video summarization,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Providence, RI, 2012, pp. 1346–1353.
-  W. Lin, Y. Zhang, J. Lu, B. Zhou, J. Wang, and Y. Zhou, “Summarizing surveillance videos with local-patch learning-based abnormality detection, blob sequence optimization, and type-based synopsis,” Neurocomputing, vol. 155, pp. 84–98, 2015.
-  B. Li and M. I. Sezan, “Event detection and summarization in sports video,” in Proc. IEEE Work. Content-Based Access of Image and Video Libraries, Kauai, HI, 2001, pp. 132–138.
-  D. Cullen, J. Konrad, and T. D. Little, “Detection and summarization of salient events in coastal environments,” in IEEE Int. Conf. Advanced Video and Signal-Based Surveillance, Beijing, China, 2012, pp. 7–12.
-  N. Ejaz, I. Mehmood, and S. W. Baik, “Efficient visual attention based framework for extracting key frames from videos,” Signal Processing: Image Communication, vol. 28, no. 1, pp. 34–44, 2013.
-  K. S. Ntalianis and S. D. Kollias, “An optimized key-frames extraction scheme based on SVD and correlation minimization,” in IEEE Int. Conf. Multimedia and Expo., Amsterdam, The Netherlands, 2005, pp. 792–795.
-  S. Lu, Z. Wang, T. Mei, G. Guan, and D. D. Feng, “A bag-of-importance model with locality-constrained coding based feature learning for video summarization,” IEEE Trans. Multimedia, vol. 16, no. 6, pp. 1497–1509, 2014.
X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier
Proc. Int. Conf. Artificial Intelligence and Statistics, vol. 15, Fort Lauderdale, FL, 2011, pp. 315–323.
-  N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting.” J. Machine Learning Research, vol. 15, no. 1, pp. 1929–1958, 2014.
-  X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proc. Int. Conf. Artificial Intelligence and Statistics, vol. 9, Sardinia, Italy, 2010, pp. 249–256.
-  D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, pp. 533–536, 1986.
-  M. Li, T. Zhang, Y. Chen, and A. J. Smola, “Efficient mini-batch training for stochastic optimization,” in Proc. ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, New York, NY, 2014, pp. 661–670.
-  D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.
-  Y. Song, J. Vallmitjana, A. Stent, and A. Jaimes, “TVSum: Summarizing web videos using titles,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Boston, MA, 2015, pp. 5179–5187.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” inProc. Int. Conf. Neural Information Processing Systems, Lake Tahoe, NV, 2012, pp. 1097–1105.
Y.-F. Ma, L. Lu, H.-J. Zhang, and M. Li, “A user attention model for video summarization,” inProc. ACM Int. Conf. Multimedia, Juan les Pins, France, 2002, pp. 533–542.