A Soft-Decision Histogram from the HSV Color Space for Video Shot Detection

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.

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An Algorithm for Segmenting Video Shot

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.3216 ◽

2014 ◽

Vol 513-517 ◽

pp. 3216-3219

Author(s):

Li Gang Tian ◽

Shao Bin Zhang ◽

Xiao Hua Sun

Keyword(s):

Color Space ◽

Video Sequences ◽

Hsv Color Space ◽

Shot Detection ◽

Space Technique ◽

Video Shot ◽

Inter Frame

Identifying gradual shot transition in video sequences is the difficult point in video shot detection. The paper presents a kind of new algorithm for segmenting video shot.It reduces the dimensionality of the images using the hsv color space technique to obtain the inter-frame differences, based on which to set Dynamic steps and get dynamic thresholds to segment video shot. The experiment results show the algorithm is available.

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Automatic video shot detection and characterization for content-based video retrieval

10.1117/12.441599 ◽

2001 ◽

Author(s):

Jifeng Sun ◽

Songye Cui ◽

Xing Xu ◽

Ying Luo

Keyword(s):

Video Retrieval ◽

Shot Detection ◽

Video Shot ◽

Content Based Video Retrieval

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Deteksi Kelayuan Pada Bunga Mawar dengan Metode Transformasi Ruang Warna Hue Saturation Intensity (HSI) dan Hue Saturation Value (HSV)

JURNAL MEDIA INFORMATIKA BUDIDARMA ◽

10.30865/mib.v5i1.2562 ◽

2021 ◽

Vol 5 (1) ◽

pp. 308

Author(s):

Dede Wandi ◽

Fauziah Fauziah ◽

Nur Hayati

Keyword(s):

Image Processing ◽

System Design ◽

Research Methods ◽

Color Space ◽

Literature Study ◽

Hsv Color Space ◽

Saturation Intensity ◽

The Rose

The rose is a plant of the genus Rosa. The rose consists of more than 100 species with various colors. In selecting and sorting roses, roses are often found that are still fresh and wilted. Based on the problems faced in roses, a system design is carried out that can detect the wilting condition of roses. By applying the HSI and HSV methods to image processing applications, it is hoped that it can help in choosing the condition of roses. With research methods through observation and literature study. To see the conditions, roses can be divided into wilted flowers and fresh flowers. In its implementation and classification, by detecting the color of roses in the HSI and HSV color space, from a total of 230 images of red and white roses that tested 200 images using HSI and HSV, the value of Range was obtained on the HSI, H = 0.240634 - 0.5, S = 0.781818 - 1, and I = 0.477124 - 1 in the Fresh category, while the HSI Wilt Category, H = 0.170495 - 0.5, S = 0.40239 - 1, I = 0.562092 - 1. and also obtained the value of Range with HSV with Fresh category H = 0.240634 - 0.5, S = 0 - 0.988235, V = 0 - 0.988235, and Wilt category H = 0.170495-0.5, S = 0 - 0.996078, V = 0 - 0.996078. With an accuracy value of the HSI and HSV of 86.9%. Therefore, it can be concluded that the detection of wilting in roses using the HSI and HSV methods is the fastest in the process using the HSI method because it reads all the min-max values.

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An Improved Algorithm of Keyframe Extraction for Video Summarization

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.225-226.807 ◽

2011 ◽

Vol 225-226 ◽

pp. 807-811

Author(s):

Zhong Qu ◽

Teng Fei Gao

Keyword(s):

Video Segmentation ◽

Video Retrieval ◽

Video Summarization ◽

Color Space ◽

Original Video ◽

Video Content ◽

Hsv Color Space ◽

Key Frame ◽

Keyframe Extraction ◽

Histogram Intersection

Video segmentation and keyframe extraction are the basis of Content-based Video Retrieval (CBVR), in which keyframe selection plays the central role in CBVR. In this paper, as the initialization of keyframe extraction, we proposed an improved approach of key-frame extraction for video summarization. In our approach, videos were firstly segmented into shots according to video content, by our improved histogram-based method, with the use of histogram intersection and nonuniform partitioning and weighting. Then, within each shot, keyframes were determined with the calculation of image entropy as a reflection of the quantity of image information in HSV color space of every frame. Our simulation results in section 4 prove that extracted key frames with our method are compact and faithful to the original video.

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Improving the video shot boundary detection using the HSV color space and image subsampling

2015 Seventh International Conference on Advanced Computational Intelligence (ICACI) ◽

10.1109/icaci.2015.7184728 ◽

2015 ◽

Cited By ~ 3

Author(s):

Fang Liu ◽

Yi Wan

Keyword(s):

Color Space ◽

Boundary Detection ◽

Shot Boundary Detection ◽

Hsv Color Space ◽

Video Shot ◽

Shot Boundary

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Histogram Generation from the HSV Color Space Using Saturation Projection

Multimedia Systems and Content-Based Image Retrieval ◽

10.4018/978-1-59140-156-8.ch005 ◽

2011 ◽

pp. 101-113

Author(s):

Shamik Sural

Keyword(s):

Color Space ◽

Hsv Color Space ◽

Generation Technique ◽

Saturation Intensity ◽

Large Databases ◽

Depth Analysis ◽

Very Large Databases ◽

Similar Images ◽

Rgb Color Space ◽

Visual Properties

In this chapter, we make an in-depth analysis of the visual properties of the HSV (Hue, Saturation, Intensity Value) color space and describe a novel histogram generation technique for color feature extraction. In this new approach, we extract a pixel feature by choosing relative weights of hue and intensity based on the saturation value of the pixel. The histogram retains a perceptually smooth color transition between its adjacent components that enables us to do a window-based smoothing of feature vectors for the purpose of effective retrieval of similar images from very large databases. The results have been compared with a standard histogram generated from the RGB color space and also with a histogram similar to that used in the QBIC system (Niblack et al., 1993).

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