A Model-Based Shot Boundary Detection Technique Using Frame Transition Parameters

Mohanta, Partha Pratim; Saha, Sanjoy Kumar; Chanda, Bhabatosh

doi:10.1109/tmm.2011.2170963

Cited by 82 publications

(48 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Use of various machine learning techniques is also quite common [23]. Decision tree based classification [20], KNN [8], fuzzy clustering [11], hidden Markov model [28], neural network [18], SVM [15,24] are few such examples. All such techniques have their own merits and demerits in terms of complexity, tuning of various parameters, proper training etc.…”

Section: Past Workmentioning

confidence: 99%

A shot detection technique using linear regression of shot transition pattern

Dutta¹,

Saha

Chanda

2014

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

Video segmentation acts as the fundamental step for various applications like, archiving, content based retrieval, copy detection and summarization of video data. Shot detection is first level of segmentation. In this work, a shot detection methodology is presented that evolves around a simple shot transition model based on the similarity of the frames with respect to a reference frame. Frames in an individual shot are very similar in terms of their visual content. Whenever a shot transition occurs a change in similarity values appears. For an abrupt transition, the rate of change is very high, while for gradual it is not so apparent. To overcome the effect of noise in similarity values, line is fit over a small window using a linear regression. Thus slope of this line exhibits the underlying pattern of transition. A novel algorithm for shot detection, hence, is developed based on the variation pattern of the similarity values of the frames with respect to a reference frame. First an algorithm is proposed, which is direct descendant of the underlying transition model and applies a threshold on the similarity values to detect the transitions. Then this algorithm is improved by utilizing the slope of linear approximation of variation in similarity values rather than the absolute values, following least square regression. Threshold on the slope is determined with a bias towards minimizing false rejection rate at the cost of false acceptance rate. Finally, a simple post-processing technique is adopted to reduce the false detection. Experiment is done with the video sequences taken from TRECVID 2001 database, action type movie video, recorded sports and news video. Comparison with few other systems indicates that the performance of the proposed scheme is quite satisfactory.

show abstract

Section: Past Workmentioning

confidence: 99%

A shot detection technique using linear regression of shot transition pattern

Dutta¹,

Saha

Chanda

2014

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

show abstract

“…In our work, shot boundaries are detected following the methodology presented in [2]. A unified model of shot boundary detection has been presented to deal with both abrupt and gradual transition.…”

Section: Shot and Key-frame Detectionmentioning

confidence: 99%

“…Our main contribution in this work are two folds: first, we have modified algorithm presented in [3] for scene detection; second, is to map the classical MST algorithm in a novel way to divide the key-frames of a scene into visually homogeneous groups and also to extract representative frame from each of these groups. The present endeavor is a continuation of our earlier work on shot segmentation [2], key-frame extraction [8] and scene segmentation [3]. However, care is taken to design the proposed algorithm such that any error committed in the earlier stages does not affect the output much.…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Static Summarization of Video Scenes Based on Minimal Spanning Tree

Mohanta

Chowdhury

Roy

et al. 2013

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. The common practice for providing a static summarized view of a video is to create a storyboard. Storyboard is the chronological arrangement of the representative frames. Shot level storyboard suffers from redundancy as in a scene constituting shots normally repeat. Also the size of such storyboard is a constraint for many application. In this work we have considered scene as the more meaningful unit. We propose a state-based scene segmentation algorithm and also a minimal spanning tree based novel method to select the representative frames for the scenes. Storyboard consisting of scene level representative frames are much more compact than shot level storyboard. Moreover, scene being the semantic unit, flow of semantic content of the video data is well preserved. Experimental result confirms the claim.

show abstract

“…On the one hand, pixel-based methods, histogram-based methods, and those based on more complex statistical analysis, are quick and easy to implement but have some drawbacks that must be taken into account [25], like selecting the best threshold according to the characteristics of the analyzed video. If the amount of motion in the video is important, high thresholds should be applied whereas, if there is no significant motion, thresholds should be low [13].…”

Section: Related Workmentioning

confidence: 99%