A Shot Boundary Detection Technique Based on Local Color Moments in YCbCr Color Space

Angadi, S. A.; Naik, Vilas

doi:10.5121/csit.2012.2307

Cited by 5 publications

(3 citation statements)

References 11 publications

(8 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…That is why, the present scheme uses YC b C r colour space. Moreover, by the use of YC b C r colour space, the influence of illumination change and shadows are also to be reduced (Angadi and Naik 2012). The difference between YC b C r and RGB is that YC b C r represents colour as brightness and two colour difference signals, while RGB represents colour as red, green and blue.…”

Section: Yc B C R Colour Spacementioning

confidence: 99%

“…This is due to the use of GA to find optimum value of threshold for key frame selection. The schemes described in Thakar et al (2012), Angadi and Naik (2012) and Dhagdi and Deshmukh (2012) use only one type of frame feature like χ2 histogram, colour moments and edge matching information, respectively for key frame detection. Those single features alone are not sufficient to maintain all the requirements as described earlier, i.e., robustness, discriminability, compactness, and low complexity.…”

Section: The Gold Standardmentioning

confidence: 99%

“…Moreover, a new pixel intensity-based method is also proposed for detecting fade transition. Angadi and Naik (2012) proposed a shot boundary detection technique based on local colour moments in YC b C r colour space. By the use of YC b C r colour space, the influence of illumination change and shadows are reduced.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Video summarisation using optimum global threshold technique based on genetic algorithm

Phadikar

Kumar²,

Phadikar

et al. 2016

IJICA

View full text Add to dashboard Cite

Most of the methods for video summarisation rely on complicated clustering algorithms that make them too computationally complex for real time applications. This paper presents an efficient approach for video summary generation that does not relay on complex clustering algorithms and does not require frame length as a parameter. The present scheme combines colour histogram and edge histogram features with optimum global thresholding to detect key frames. The optimum threshold is selected based on genetic algorithm (GA) to increase the performance of the proposed system. For each shot, key frames are extracted and similar key frames are eliminated. Experimental results duly support those claims.

show abstract

Section: Yc B C R Colour Spacementioning

confidence: 99%

Section: The Gold Standardmentioning

confidence: 99%

See 1 more Smart Citation

Video summarisation using optimum global threshold technique based on genetic algorithm

Phadikar

Kumar²,

Phadikar

et al. 2016

IJICA

View full text Add to dashboard Cite

show abstract

Shot-Net: A Convolutional Neural Network for Classifying Different Cricket Shots

Foysal

Islam

Karim

et al. 2019

Communications in Computer and Information Science

View full text Add to dashboard Cite

Video shot-boundary detection: issues, challenges and solutions

Kar,

Kanungo,

Mohanty

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

The integration of high data transmission rates and the recent digital multimedia technology, paves the way to access a huge amount of video over the internet, in seconds. Additionally, uploading videos to different websites is no more confined to expert software professionals resulting in duplication of video data which led to exorbitant growth of multimedia information in cyberspace in a short span of time. This necessitates the development of efficient data management techniques including storage, searching and annotation mechanism. Automatic shot boundary detection is considered to be the first and foremost step towards such management. It is a booming area of research gaining attention in the domain of image processing, computer vision and pattern recognition. In this review paper, we present a detailed description of the methods and algorithms of shot boundary detection, reported in the last two decades. This review shows that using multiple features performs well in comparison to using only a single feature in the shot boundary detection problem although it leads to higher complexity. The major sources of disturbance in the boundary detection are the sudden illumination variation and presence of high motion in the video. An adaptive threshold outperforms a single global threshold in the boundary detection problem and the threshold requirement can be avoided through learning based strategies at the cost of larger training data and higher computation time. Moreover the present review includes a critical analysis of relative merits and demerits of existing algorithms and finally opens promising research directions in the area.

show abstract

A Shot Boundary Detection Technique Based on Local Color Moments in YCbCr Color Space

Abstract: ABSTRACT

Cited by 5 publications

References 11 publications

Video summarisation using optimum global threshold technique based on genetic algorithm

Video summarisation using optimum global threshold technique based on genetic algorithm

Shot-Net: A Convolutional Neural Network for Classifying Different Cricket Shots

Video shot-boundary detection: issues, challenges and solutions

Contact Info

Product

Resources

About