AVCD-FRA: A novel solution to automatic video cut detection using fuzzy-rule-based approach

Video shot boundary detection is the initial and fundamental step towards video indexing, browsing and retrieval. Great efforts have been paid on developing accurate shot boundary detection algorithms. However, the high computational cost in shot detection becomes a bottleneck for real-time applications. The problem of making a balance between detection accuracy and speed is addressed in this paper, and a novel fast detection framework is presented. The general framework that employs pre-processing techniques can improve both detection speed and precision. In the pre-processing stage, adaptive local thresholding is adopted to classify non-boundary segments and candidate segments that may contain shot boundaries. The candidate segments are refined using bisection-based comparisons to eliminate non-boundary frames. Only refined candidate segments are preserved for further detections; hence, the speed of shot detection is improved by reducing detection scope. Moreover, prior knowledge about each possible shot boundary such as its type and duration can be obtained in the pre-processing stage, which can accelerate the consequent hard cut and gradual transition detections. Experimental results indicate that the proposed framework is effective in accelerating the shot detection process, and it can also achieve excellent detection accuracies.

One of the essential pre-processing steps of semantic video analysis is the video shot boundary detection (SBD). It is the primary step to segment the sequence of video frames into shots. Many SBD systems using supervised learning have been proposed for years; however, the training process still remains its principal limitation. In this paper, a multi-modal visual features-based SBD framework is employed that aims to analyze the behaviors of visual representation in terms of the discontinuity signal. We adopt a candidate segment selection that performs without the threshold calculation but uses the cumulative moving average of the discontinuity signal to identify the position of shot boundaries and neglect the non-boundary video frames. The transition detection is structurally performed to distinguish candidate segment into a cut transition and a gradual transition, including fade in/out and logo occurrence. Experimental results are evaluated using the golf video clips and the TREC2001 documentary video data set. Results show that the proposed SBD framework can achieve good accuracy in both types of video data set compared with other proposed SBD methods.

Shot boundary detection has attracted much more research interesting in recent years, but many of the algorithms proposed in the literatures are not robust to flash lamp and motion of camera and object. This paper present a novel shot boundary detection algorithm based on graph theory. It can detect cut and gradual shot and distinguish them, and it is not intensive to all of those interference factors. At first the feature of color is extracted, the dissimilarity of video frames is defined. Then the video frames are divided into several different groups through performing graph-theoretical algorithm. According to cut and gradual changes have the different characters on the two successive frames belong to different groups, it detect cut and gradual shot. The efficiency of the proposed algorithm is extensively tested on movie, sports and other videos. The experiments results indicate the method had a high accurate rate in both cut shot detection and gradual shot detection.

The video shot boundary detection is an area, where many techniques and methods have been suggested and have claimed to perform consistently on the videos like television broadcasting of different sports events, talk shows & news telecast. The movies contain a large amount of visual information varying in arbitrary manner; it requires a highly vigorous procedure for automatic shot boundary detection. The main goal of this research is to develop an adaptive & robust algorithm for segmentation & classification of movie videos. In this paper, we described a novel approach for shot boundary detection which is totally adaptive & changes the threshold value as per the information change. We have defined the novel feature which can uniquely define color as well as edge & frequency details of a frame. It is able to detect not only abrupt transitions like hard cuts but also special effects like wipes, fades, and dissolves as well in different movies.

Video shot boundary detection (SBD) is the first step of video analysis, summarization, indexing, and retrieval. In SBD process, videos are segmented into basic units called shots. In this paper, a new SBD method is proposed using color, edge, texture, and motion strength as vector of features (feature vector). Features are extracted by projecting the frames on selected basis vectors of Walsh-Hadamard transform (WHT) kernel and WHT matrix. After extracting the features, based on the significance of the features, weights are calculated. The weighted features are combined to form a single continuity signal, used as input for Procedure Based shot transition Identification process (PBI). Using the procedure, shot transitions are classified into abrupt and gradual transitions. Experimental results are examined using large-scale test sets provided by the TRECVID 2007, which has evaluated hard cut and gradual transition detection. To evaluate the robustness of the proposed method, the system evaluation is performed. The proposed method yields F1-Score of 97.4% for cut, 78% for gradual, and 96.1% for overall transitions. We have also evaluated the proposed feature vector with support vector machine classifier. The results show that WHT-based features can perform well than the other existing methods. In addition to this, few more video sequences are taken from the Openvideo project and the performance of the proposed method is compared with the recent existing SBD method.

Video shot boundary detection plays an every important role in video processing. It is the first step toward video content analysis and content-based video retrieval. We develop a novel approach for video shot boundary detection based on supervised learning. Our method consists in first extracting video frame feature using a supervised kernel non-locality preserving projections, then video frames are split into abrupt transitions, gradual transitions or normal frames using two cascaded Localized-SVM classifiers. Experimental results show the effectiveness of our method.

In sports programs, there are many special cases making shot boundary detection difficult. Targeted for these special cases, not be considered by existing work, this paper presents a shot boundary detection scheme to detect both cuts and gradual transition efficiently. For shot detection, the algorithm is proposed to resist continuous flashes, camera occlusion or image blur that have not been considered before. For gradual transition detection, a unified method is presented to detect various transitions or special effects, together with an algorithm to reduce the false positives caused by fast camera or object motions. The cut detection and gradual transition detection are implemented serially to avoid repeated detection operations. Compared with existing typical works, the proposed scheme obtains higher correct detection rate and fast detection speed, and is more suitable for sports program analysis.

Shot and scene boundary detection is a primary and an important step towards all the video condensation techniques. In this paper a simple but automatic and efficient scene and shot boundary detection is proposed based on energy minimization approach. A shot differs in its background from another. The background has a temporal continuity throughout the shot, we have invoked this fact in our algorithm. We formulate the extraction of Video shot as an energy minimization problem in which the desire to extract the shot with the common background is represented by an energy term. The energy minimized approach optimizes to minimize the discontinuity in the background edges and color. We have proposed different algorithms for different kinds of shot transitions(cut, fade-in, fade-out, dissolve and wipe) to help us to get a better solution for shot boundary detection problem. We have demonstrated our method with a variety of videos which is capable of accurately detecting shot transitions.

Due to the growth of internet and multimedia technology, there is a tremendous need for an efficient method of automatic video retrieval and storage process. Automatic annotation is one of the key solutions for the automatic retrieval or search of a particular scene based frames or a particular scene based videos from the huge video database. Video segmentation based on shot boundary detection (Cut detection) is the fundamental step for the content based text annotation or video data analysis. In this paper, a new automatic cut detection method is proposed based on the local binary pattern feature. The proposed algorithm is tested with six test videos and its efficacy is validated with few existing popular approaches.

In this work, we present a system for video shot boundary detection and key-frame extraction from video sequences based on colour histograms. The growth of archived video material has made automated indexing and browsing tools necessary. The aim of this paper is to provide a framework for such a video indexing and browsing tool. The proposed system segments video sequences based on colour similarity, and then extracts key-frames by clustering the images. Computations of colour histogram differences and self-similarity modelling have been performed in segmentation process. We have extracted three-, four- and five-frames video summaries with unsupervised k-means clustering. We have also dynamically determined the number of key-frames representing the video content without any prior information based on dominant set clustering. Our results show that our approach is successful in both segmentation and key-frame extraction processes.

In this chapter, we describe a histogram with soft decision using the Hue, Saturation, Intensity Value (HSV) color space for effective detection of video shot boundaries. In the histogram, we choose the relative importance of hue and intensity depending on the saturation of each pixel. In traditional histograms, each pixel contributes to only one component of the histogram. However, we suggest a soft-decision approach in which each pixel contributes to two components of the histogram. We have done a detailed study of the various frame-to-frame distance measures using the proposed histogram and an Red, Green, Blue (RGB) histogram for video shot detection. The results show that the new histogram has a better shot-detection performance for each of the distance measures. A Web-based application has been developed for video retrieval, which is freely accessible to interested users.

Video Shot Boundary Detection (SBD) is the fundamental process towards video summarization and retrieval. A fast and efficient SBD algorithm is necessary for real-time video processing applications. Extensive work has focused on accurate shot boundary detection at the expense of demanding computational costs. In this paper, we propose a fast SBD approach that reduces the computation pixel-wise and frame-wise while still giving satisfactory accuracy. The proposed approach substantially speeds up the computation through reducing both detection region and scope. Color histogram and mutual information are used together to measure the difference between frames. Corner distribution of frames is utilized to exclude most of false boundaries. We conduct extensive experiments to evaluate the proposed approach, and the results show that our approach can not only speed up SBD, but also detect shot boundaries with high accuracy in both Cut (CUT) and Gradual Transition (GT) boundaries.

The paper proposes a shot boundary detection system using Gist and local descriptor. Gist provides the perceptual and conceptual information of a scene. The proposed system can be roughly divided into three steps. The first step consists of forming of groups of frames by calculating the correlation of the Gist features between consecutive frames of the video. Secondly, abrupt transitions are found out using the group (G), MSER and a threshold (for abrupt separately, \(th_{cut}\)). And lastly, gradual transitions of the video are found using triangular pattern matching. We have performed the experiment on TRECVid 2001 and 2007 dataset. The novel contribution of this paper is that the proposed system shows an activity-based shot boundary detection where only the possible transition regions of a video are considered for shot detection. This approach reduces the computational complexity by processing the transition regions only. We have achieved better results in terms of F1, precision and recall, when compared to previously published approaches.

ABSTRACTThe proposed content-based video retrieval method in this study uses feature extraction, shot boundary detection, shot noise filter, shot cluster, the key feature set of shot extraction and shot classification. Shot boundary detection is conducted using a weight selection genetic algorithm. In order to verify the video retrieval method in this paper, three video databases were used in the experiment. These video databases are used to verify the shot boundary detection, the shot classification and video retrieval of this study. A series of comparisons and analyses were then conducted using the above video databases. The experiment results showed that the shot boundary detection method exhibits better performance than the other compared methods. Moreover, the video retrieval experiment results showed that the proposed method is able to precisely catch a shot in the search video, and do so with reduced video retrieval time.

With the increase of multimedia devices on internet, enormous number of videos are being added as part of digital content. There is a huge challenge in retrieval of these videos as mostly the videos are kept in unstructured form. Intended users try to retrieve video content as per the relevancy and need. Shot boundary detection is a significant and critical approach in the domain of digital video processing. It is the foremost critical job of content-based video retrieval and indexing. In this paper, a novel approach for shot boundary detection algorithm based on Differential evolution algorithm (DE) with SVM classifier is proposed. In this method we first calculate the curves difference of U-component histograms as the feature of difference between video frames. In the next step, Slide-Window Mean Filter to filter difference curves and SVM Classifier applying DE to detect and classify the shot transitions. The well-known TRECVID 2005, 2006, and 2007 datasets are used to test the performance of our proposed approach. The result shows the superior performance of our proposed approach, and this method can achieve high recall, precision rate, accuracy, and better computation time.