A novel shot detection algorithm based on graph theory

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

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.

An adaptive threshold shot detection algorithm based on improved block color features is proposed in this paper. This paper adopts an improved block color feature extraction method based on equal area of rectangular ring. Sub-block accumulative color histogram is extracted as color features and different weight for different rectangle rings is set in order to highlight the central part of frame. Then, adaptive threshold of detecting abrupt shot and gradual shot is calculated, and different detection modules is used according to the distance of the characteristics between frames. In the abrupt shots detection, several frames' frame difference and the edge shape features between adjacent frames are calculated to detect the flash. In the gradual shots detection, the discontinuous frame differences between the current frame and back frames are used to detect the boundary of the gradual shot. The experimental results show that this method has better effect to different types of video.

Shot boundary detection is essentially to detect the position of frames where the shot changes. It has been actively studied in video analysis and management for convenience, which becomes a key technique with the rapid proliferation of rich and diverse videos. With respect to the complex characteristics of different shots in varying length and content variation property, in this paper we present a two stage method for shot boundary detection (TSSBD) which distinguishes abrupt shot by fusing color histogram and deep features, and locate gradual shot changes with C3D-based deep analysis. Abrupt shot changes are detected firstly as it occurs between two frames, which divides the complete video into segments containing gradual transitions; Over these video segments, gradual shot change detection is implemented using 3D-convolutional neural network, which classifies clips into specific gradual shot change types; Finally, an effective merging strategy is proposed to locate positions of gradual shot transitions. The experimental analysis illustrates that the proposed progressive method is capable of detecting both abrupt shot transitions and gradual shot transitions accurately.

Video segmentation is fundamental to a number of applications related to video retrieval and analysis. Shot change detection is the initial step of video segmentation and indexing. There are two basic types of shot changes. One is the abrupt change or cut, and the other is the gradual shot transition. The smooth variations of the video feature values in a gradual transition produced by the editing effects are often confused with those caused by camera or object motions. To overcome this difficulty, it is reasonable to estimate the motions and suppress the disturbance caused by them. In this thesis, we explore the possibility to exploit motion and illumination estimation in a video sequence to detect both abrupt and gradual shot changes. A generalized optical flow constraint that includes an illumination parameter to model local illumination changes is employed in the motion and illumination estimation. An iterative process is used to refine the generalized optical flow constraints step by step. A robust measure that is the likelihood ratio of the corresponding motion-compensated blocks in the consecutive frames is used for detecting abrupt changes. For the detection of gradual shot transitions, we compute the average monotony of intensity variations on the stable pixels in the images in a twin-comparison framework. We test the proposed algorithm on a number of video sequences in TREC 2001 and compare the detection results with the best results reported in the TREC 2001 benchmark. The comparisons indicate that the proposed shot change detection algorithm is competitive against the best existing algorithms.

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.

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.

The Shot Boundary Detection (SBD) is an early step for most of the video applications involving understanding, indexing, characterization, or categorization of video. The SBD is temporal video segmentation and it has been an active topic of research in the area of content based video analysis. The research efforts have resulted in a variety of algorithms. The major methods that have been used for shot boundary detection include pixel intensity based, histogram-based, edge-based, and motion vectors based, technique. Recently researchers have attempted use of graph theory based methods for shot boundary detection. The proposed algorithm is one such graph based model and employs graph partition mechanism for detection of shot boundaries. Graph partition model is one of the graph theoretic segmentation algorithms, which offers data clustering by using a graph model. Pair-wise similarities between all data objects are used to construct a weighted graph represented as an adjacency matrix (weighted similarity matrix) that contains all necessary information for clustering. Representing the data set in the form of an edge-weighted graph converts the data clustering problem into a graph partitioning problem. The algorithm is experimented on sports and movie videos and the results indicate the promising performance.

As the volume of digital video captured and stored continues to increase, research efforts have focused on content management systems for video indexing and retrieval applications. A first step in generic video processing is shot boundary detection. This paper addresses a novel algorithm for abrupt shot (cut/pause) detection-especially on frames with similar statistics-based on the wavelet transform and content entropy. The algorithm has been successfully tested on some video categories including sport and live videos. Its quantitative performance has been compared to other known methods including pixel, histogram, frequency domain and statistics difference. In each test, the proposed wavelet method outperforms the others.

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.

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.

We propose a method of shot boundary detection based on the co-occurrence of global motion in video stream. In addition to the conventional features based on appearance and local motion, we apply ST (Space-Time) patch analysis for detecting global motion in video stream. And then we perform shot boundary detection by constructing AdaBoost classifiers which represent the co-occurrence of global motion and the conventional features. Experimental results show that our method had 3.8% higher F-measure value than that of the conventional method for gradual shot boundary detection.

In this paper we propose a novel and complete video structuring/ segmentation framework, which includes shot boundary detection, key-frame selection and high level clustering of shots into scenes. In a first stage, an enhanced shot boundary detection algorithm is proposed. The approach extends the state-of-the-art graph partition model and exploits a scale space filtering of the similarity signal which makes it possible to significantly increase the detection efficiency, with gains of 7,4% to 9,8% in terms of both precision and recall rates. Moreover, in order to reduce the computational complexity, a two-pass analysis is performed. For each detected shot we propose a leap keyframe extraction method that generates static summaries. Finally, the detected keyframes feed a novel shot clustering algorithm which integrates a set of temporal constraints. Video scenes are obtained with average precision and recall rates of 85%.

Recent advances in computing, communication, and sensor technology are pushing the development of many new applications. This trend is especially evident in pervasive computing. The content-based video analysis is one example of this innovation. Shot boundary detection servers as an important step to structure the contents of videos, many research efforts have been devoted to this field. A novel approach is proposed to detect cuts and gradual shots. An effective feature selection mechanism is introduced using shot boundary coefficient (SBC) combined with HSV divisional histogram. SBC is derived from a model called average sliding-window difference. It is a novel way to enlarge frame difference. SBC can remove the factors affected by camera/object motion, illumination and other artifacts on the curve of frame-wise histogram difference. Each area of the frame has different weight according to its respective importance. In order to improve the speed, at the beginning of the program, we adopt n rank Newton interpolation formula to deflate the frame size. We use Self-Organizing Map (SOM) Network to detect the abrupt shot. It can avoid the threshold completely. Gauss Model is selected to detect gradual shots. Based on the set of abrupt shots, we qualify the candidate set of the gradual shot furthermore. The experimental results have shown that a good compromise between speed and accuracy is achieved. It also has good noise tolerance characteristics.

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.