MINMAX Video Summarization under Equality Principle (original) (raw)

An improved sub-optimal video summarization algorithm

2010

During the last few years the amount of digital video content has been increasing exponentially as a result of the proliferation of media sources like digital TV, streaming video internet sites like YouTube and wider availability of digital video cameras. The video data volume is so large that the only way a user can browse these libraries is through the use of timecondensation techniques. Video summarization achieves timecondensation by choosing a sub-set of frames of the original video creating a summary hopefully representative of the source video. The frame selection process can be directed according to different principles, based on either subjective or objective frame-relevance measures. Previous works have used dynamic programming (DP) and greedy approaches to choose the frames that make up the video summary. We present an algorithm that performs better than the greedy solution achieving a performance simplicity.

A Novel Key Frame Extraction Approach for Video Summarization

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2016

Video summarization is a principal task in video analysis and indexing algorithms. In this paper we will present a new algorithm for video key frame extraction. This process is one of the basic procedures for video retrieval and summary. Our new approach is based on interest points description and repeatability measurement. Before key frame extraction, the video should be segmented into shots. Then, for each shot, we detect interest points in all images. After that, we calculate repeatability matrix for each shot. Finally, we apply PCA and HAC to extract key frames.

An innovative algorithm for key frame extraction in video summarization

Journal of Real-Time Image Processing, 2006

Video summarization, aimed at reducing the amount of data that must be examined in order to retrieve the information desired from information in a video, is an essential task in video analysis and indexing applications. We propose an innovative approach to the selection of representative (key) frames of a video sequence for video summarization. By analyzing the differences between two consecutive frames of a video sequence, the algorithm determines the complexity of the sequence in terms of changes in the visual content expressed by different frame descriptors. The algorithm, which escapes the complexity of existing methods based, for example, on clustering or optimization strategies, dynamically and rapidly selects a variable number of key frames within each sequence. The key frames are extracted by detecting curvature points within the curve of the cumulative frame differences. Another advantage is that it can extract the key frames on the fly: curvature points can be determined while computing the frame differences and the key frames can be extracted as soon as a second high curvature point has been detected. We compare the performance of this algorithm with that of other key frame extraction algorithms based on different approaches. The summaries obtained have been objectively evaluated by three quality measures: the Fidelity measure, the Shot Reconstruction Degree measure and the Compression Ratio measure.

MINMAX optimal video summarization

IEEE Transactions on Circuits and Systems for Video Technology, 2005

The need for video summarization originates primarily from a viewing time constraint. A shorter version of the original video sequence is desirable in a number of applications. Clearly, a shorter version is also necessary in applications where storage, communication bandwidth and/or power are limited. In this paper, our work is based on a MINMAX optimization formulation with viewing time, frame skip and bit rate constraints. New metrics for missing frame and video summary distortions are introduced. Optimal algorithm based on dynamic programming is presented along with experimental results.

Comparative analysis of shot boundary detection algorithms for video summarization

CSI Transactions on ICT, 2016

Video summarization is very effective process in order to extract the essential and necessary information from huge videos and convert them into summarized videos. In this paper, we study and evaluate the comparative analysis of shot boundary detection algorithms of video summarization. The histogram based and edge based algorithms already exists are studied and compared with the new proposed improved histogram algorithm. The experimental evaluation validates the proposed approach returning most representative keyframes. The graphical representation of keyframes through above mentioned algorithms makes it clearer that new proposed algorithm delivers better and respectable results.

Dynamic key-frame extraction for video summarization

2004

We propose an innovative approach to the selection of representative frames of a video shot for video summarization. By analyzing the differences between two consecutive frames of a video sequence, the algorithm determines the complexity of the sequence in terms of visual content changes. Three descriptors are used to express the frame's visual content: a color histogram, wavelet statistics and an edge direction histogram. Similarity measures are computed for each descriptor and combined to form a frame difference measure. The use of multiple descriptors provides a more precise representation, capturing even small variations in the frame sequence. This method can dynamically, and rapidly select a variable number of key frame within each shot, and does not exhibit the complexity of existing methods based on clustering algorithm strategies.

A Survey on Key Frame Based Video Summarization Techniques

2017

The large amount of videos usage increase the volume of data, more time to access and more man power is required. Video summarization is the solution for this problem. Summarized video can be used to review the important aspect of particular video, indexing and faster browsing. Video summarization techniques are classified into key frame based classification and skim based classification. This paper focuses on process and techniques of key frame based classification.

Dynamic key-frame extraction for video summarization

Internet Imaging VI, 2005

We propose an innovative approach to the selection of representative frames of a video shot for video summarization. By analyzing the differences between two consecutive frames of a video sequence, the algorithm determines the complexity of the sequence in terms of visual content changes. Three descriptors are used to express the frame's visual content: a color histogram, wavelet statistics and an edge direction histogram. Similarity measures are computed for each descriptor and combined to form a frame difference measure. The use of multiple descriptors provides a more precise representation, capturing even small variations in the frame sequence. This method can dynamically, and rapidly select a variable number of key frame within each shot, and does not exhibit the complexity of existing methods based on clustering algorithm strategies.

Key Frames Extraction and Video Summarization Based On Histogram

2014

with the advent of digital multimedia, a lot of digital content such as conference, movies, and video lecturers, news, shows and sports events is widely available. Also, due to the advances in digital content distribution (direct-to-home satellite reception) and digital video recorders, this digital content can be easily recorded. However, the user may NOT have sufficient time to watch the entire video (Ex. User may want to watch just the highlights of an occurrence) or the whole of video content may not be of interest to the user(Ex. Soccer or cricket match video). In such cases, the user may just want to view the summary of the video instead of watching the whole video. In this paper video summarization is based frame extraction in which whole video is convert into the shots (segmentation) and then select frames which is part of video summary and this will be done by taking different parameters like entropy change, histogram level, audio level etc. and then select frames for make ...

Feature based Information Extraction for Generic Video Summarization

IJCA Proceedings on International Conference on Recent Advances and Future Trends in Information Technology (iRAFIT 2012), 2012

Video summarization plays a very significant role in navigating a video, to understand its information or to search the required event information. Our proposed research work minimizes the time required for processing each of the video frames firstly, by reducing their effective size, and then it is followed by an efficient technique for generating the summarized video. The information contained in a frame is extracted using object and motion based features where the object based feature helps to evaluate the importance of the given frame compared to its neighboring frames and the motion based feature helps to estimate the dynamism of the frame. Disturbance Ratio [DR] based measurement is used in the next step to select the shot boundary, key frame and summary generation. The results of the proposed summarization methodology show the efficiency of our algorithm, which is further supported by a comparative study of the related research works.