Frame clustering technique towards single video summarization
01 Aug 2016-pp 1-5
TL;DR: This paper presents a novel frame clustering approach for generating very concise summaries by grouping all frames of similar concepts together irrespective of their occurrence sequence and demonstrates the efficiency of this approach in generating concise video summaries.
Abstract: Recent advances in technology, multimedia and social networking sites have led to a massive growth in web video content available for the general population. This results in information overload and management problem of the same. In this context, video summarization plays an important role as it aims to reduce the content size of video and yet present the important semantic concepts in the video. This gives an opportunity to reorganize video content in most succinct form for efficient and on- demand user consumption. Video summarization in its true sense is a hard problem as it involves domain specific semantic understanding of video content and user expectations. Most of the existing approaches relies on segmenting video into contiguous shots & selecting one or more keyframes from each shot and present these keyframes as summary. Such approaches may work well if independent concepts in video appear only once. However, in videos where same concepts are repeated multiple times, these existing approaches may pick repeating summary frames belonging to same concepts. In this paper, we present a novel frame clustering approach for generating very concise summaries by grouping all frames of similar concepts together irrespective of their occurrence sequence. The proposed approach is aimed towards large videos in domains like travel guide, documentaries, dramas where video revolves around few repeating concepts. The approach utilizes multiple video features in a generic way for frame-similarity determination and is extensible for multi-video summarization. Experimental comparative results substantiate the efficiency of the proposed approach in generating concise video summaries on videos with repeating concepts.
Citations
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Proceedings Article•
08 Mar 201741 citations
26 Apr 2017
TL;DR: An attempt has been made in this study to empirically evaluate the effectiveness of data mining techniques in video summarization to indicate that clustering based video summarizations techniques can be effectively used for generating video summaries.
Abstract: Identification of relevant frames from a video which can then be used as a summary of the video itself, is a challenging task. An attempt has been made in this study to empirically evaluate the effectiveness of data mining techniques in video summarization. Video Summarization systems based on histogram and entropy features extracted from three different color spaces: RGB, HSV and YCBCR and clustered using K-Means, FCM, GM and SOM were empirically evaluated on fifty video datasets from the VSUMM [1] database. Results indicate that clustering based video summarizations techniques can be effectively used for generating video summaries.
11 citations
01 Feb 2020
TL;DR: The proposed solution aims at selecting keyframes from the video based on two criteria i.e. each object should appear within the scope of frame and each object must be visually presentable and must be closer to each other so that it could only show the related activities for ex.
Abstract: Today, System comprised of Surveillance cameras has become very useful and important in the every field, Mostly in the security industry. Also, Many numbers of surveillance cameras get added to the networks of surveillance or system every year as need and importance of surveillance cameras is increasing day by day. Video recorded from these surveillance cameras are large in size which require huge amount of time for monitoring and large storage space. Hence, there is a need of video summarization which has become very prominent since the last ten years because of the huge amount of available digital video content [3]. An algorithm we used for video summarization typically takes surveillance video as an input and extract a set of important frames or key-frames which is useful to represent the entire video content which are effectively more concise as compared to the original input video and convey semantic meaning. So, Our proposed solution aims at selecting keyframes from the video based on two criteria i.e. each object should appear within the scope of frame and each object should be visually presentable and must be closer to each other so that it could only show the related activities for ex. Summarization of video captured from ATM room camera should only display the part where user is interacting with the machine. So such a key frames are then used in final summarization.
4 citations
Cites background or methods from "Frame clustering technique towards ..."
...Taking consumer video as reference which usually has unclear shot boundaries and low-quality video or uncleared frames, two-step method is used where the first step is to skim a video and the second step is to perform a content-aware clustering with the selection of key-frame [12]....
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...shot boundaries and at the end one representative frame or key frame is selected from each shot for summarization [12]....
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TL;DR: A novel approach based on clustering of video frames based on their feature vectors based on the similarity factor between these vectors is presented, which outperform some of the established methodologies and serve the summarization purpose.
Abstract: With ever-growing utilization of online and offline videos and increasing video content, Video Summarization serves as the best aid for video browsing. It involves domain explicit semantic comprehension of a video and understanding of user expectations. Generally, video summarization systems include extracting video features, analyzing the visual variations and selecting video frames. Over the years, various methodologies have been developed for the same. Different supervised and unsupervised algorithms have been established and these models have been trained on various factors or various rewards. The challenges these methods face stand as a motivation for the approach this paper discusses. Like in many cases, summary frames may be repeated if some scene or concept appears more than once. This paper presents a novel approach based on clustering of video frames based on their feature vectors. The clustering takes into consideration the semantic factor of video frames. Each concept cluster gives a representative frame which then forms the summary set, here concept cluster refers to the independent entity present in a video which can be easily distinguished by another concept or entity. This entity can be a scene of a mountain or different persons. It also aims to increase system performance by removing the redundancy. The system is developed using a CNN for feature extraction and a clustering algorithm that takes into consideration the similarity factor between these vectors. The model is evaluated on the measures Precision and Recall and tested on the VSUMM dataset. The results outperform some of the established methodologies and serve the summarization purpose.
2 citations
Cites methods from "Frame clustering technique towards ..."
...Some approaches consider basic color and edge features [2] while there is also a use of Bag-of-Features model [6]....
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TL;DR: In this article , the authors conjointly se machine learning techniques to develop video summarization however it had a drawback of the necessity of high-performance devices as the training period can be huge for data sets that contain videos.
Abstract: Automatic video summarization technique using natural language processing. The significance of automated video summarization is large within the new generation of big data. A video summarization helps in economical storage and additionally fast of enormous assortment of videos while not losing the necessary. Every video may be an assortment of many frames and every of those frames is truly images, and every second of ordinary video consists of twenty-four frames. The projected technique generates the summarized videos with the assistance of subtitles. We will conjointly se Machine learning techniques to develop video summarization however it had a drawback of the necessity of high-performance devices as the training period can be huge for data sets that contain videos. Even after we train using data sets that contain pictures, it takes some amount of time to process the dataset or to relinquish the acceptable results. So, it would be harder to use machine learning algorithms when put next to natural language processing.KeywordsVideo summarizationSubtitlesMachine learningNatural language processing
1 citations
References
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Additional excerpts
...input and output [15][16]....
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TL;DR: A new technique for key frame extraction is presented that uses an aggregation mechanism to combine the visual features extracted from the correlation of RGB color channels, color histogram, and moments of inertia to extract key frames from the video.
165 citations
TL;DR: The quantized histogram statistical texture features are extracted from the DCT blocks of the image using the significant energy of the DC and the first three AC coefficients of the blocks for the effective matching of images in the compressed domain.
Abstract: The effective content-based image retrieval (CBIR) needs efficient extraction of low level features like color, texture and shapes for indexing and fast query image matching with indexed images for the retrieval of similar images. Features are extracted from images in pixel and compressed domains. However, now most of the existing images are in compressed formats like JPEG using DCT (discrete cosine transformation). In this paper we study the issues of efficient extraction of features and the effective matching of images in the compressed domain. In our method the quantized histogram statistical texture features are extracted from the DCT blocks of the image using the significant energy of the DC and the first three AC coefficients of the blocks. For the effective matching of the image with images, various distance metrics are used to measure similarities using texture features. The analysis of the effective CBIR is performed on the basis of various distance metrics in different number of quantization bins. The proposed method is tested by using Corel image database and the experimental results show that our method has robust image retrieval for various distance metrics with different histogram quantization in a compressed domain.
110 citations
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19 Jul 2010
TL;DR: This paper compares major state-of-the-art similarity measures applicable to flexible feature signatures with respect to their qualities of effectiveness and efficiency and study the behavior of the similarity measures by discussing their properties.
Abstract: Determining similarities among data objects is a core task of content-based multimedia retrieval systems. Approximating data object contents via flexible feature representations, such as feature signatures, multimedia retrieval systems frequently determine similarities among data objects by applying distance functions. In this paper, we compare major state-of-the-art similarity measures applicable to flexible feature signatures with respect to their qualities of effectiveness and efficiency. Furthermore, we study the behavior of the similarity measures by discussing their properties. Our findings can be used in guiding the development of content-based retrieval applications for numerous domains.
87 citations
"Frame clustering technique towards ..." refers result in this paper
...Combined features always generate better results as compared to single feature [9][10]....
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