Is there a best color space for color image characterization or representation based on Multivariate Gaussian Mixture Model?

2009 ◽  
Vol 113 (8) ◽  
pp. 867-877 ◽  
Author(s):  
Olivier Alata ◽  
Ludovic Quintard
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Color Image ◽  
Color Space ◽  
Gaussian Mixture ◽  
Multivariate Gaussian

scholarly journals Deteksi Api pada Video dengan Gaussian Mixture Model Untuk Deteksi Gerakan Dan Segmentasi Warna Api Dalam Ruang Warna YCbCr

2021 ◽  
Vol 3 (1) ◽  
pp. 108-119
Author(s):  
Ristirianto Adi ◽  
I Gede Pasek Suta Wijaya
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Processing Speed ◽  
Color Space ◽  
Gaussian Mixture ◽  
Color Segmentation ◽  
Test Results ◽  
Ycbcr Color Space ◽  
Property Loss ◽  
Better Than

Fire is a disaster that can endanger lives and cause property loss. The solution to detect fire that is commonly used today is to use a sensor. Fire sensors can be used together with surveillance cameras (CCTV) which are now being installed in many office buildings. This study tries to build a model for detecting fire in video with a digital image processing approach using the Gaussian Mixture Model for motion detection and fire color segmentation in the YCbCr color space. The model is then tested with metrics for accuracy, precision, recall, and processing speed. The dataset used is in the form of videos with small, medium, large fire sizes, and videos that only have fire-like objects. The test results show that the algorithm is able to detect fire when the size of the fire is not too small or the position of the fire is close enough to the camera. For videos with a resolution of 800x600 and a framerate of 30 fps, it can achieve 66.89% accuracy, 73.77% precision, and 66.66% recall. The performance during the day is relatively better than at night. Algorithm processing speed is too slow to be implemented in real-time

scholarly journals High-dimensional Probabilistic Fingerprinting in Wireless Sensor Networks based on a Multivariate Gaussian Mixture Model

2018 ◽  
Author(s):  
Yan Li ◽  
Simon Williams ◽  
Bill Moran ◽  
Allison Kealy ◽  
Guenther Retscher
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Low Cost ◽  
Classification Problem ◽  
Gaussian Mixture ◽  
High Dimensional ◽  
Mobile Users ◽  
Prototype Model ◽  
Location Awareness ◽  
Multivariate Gaussian

The extensive deployment of wireless infrastructure provides a low-cost way to track mobile users in indoor environment. This paper demonstrates a prototype model of an accurate and reliable room location awareness system in a real public environment, where three typical problems arise. First, a massive number of access points (APs) can be sensed leading to a high-dimensional classification problem. Second, heterogeneous devices record different received signal strength (RSS) levels due to the variations in chip-set and antenna attenuation. Third, APs are not necessarily visible in every scanning cycle leading to missing data. This paper presents a probabilistic Wi-Fi fingerprinting method in a hidden Markov model (HMM) framework for mobile user tracking. Considering the spatial correlation of the signal strengths from multiple APs, a Multivariate Gaussian Mixture Model (MVGMM) is fitted to model the probability distribution of RSS measurements in each cell. Furthermore, the unseen property of invisible AP has been investigated in this research, and demonstrated the efficiency of differentiation between cells. The proposed system is able to achieve comparable localization performance. The filed test results present a reliable 97% localization room level accuracy of multiple mobile users in a real university campus WiFi network without any prior knowledge of the environment.

Color Quantization Based on Gaussian Mixture Mode

2012 ◽  
Vol 457-458 ◽  
pp. 650-654
Author(s):  
Qiu Chun Jin ◽  
Xiao Li Tong
Keyword(s):  
Image Analysis ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Color Image ◽  
Gaussian Mixture ◽  
Experimental Results ◽  
Color Quantization ◽  
Quantization Method ◽  
Color Palette ◽  
Image Quantization

Color quantization is an important technique for image analysis that reduces the number of distinct colors for a color image. A novel color image quantization algorithm based on Gaussian mixture model is proposed. In the approach, we develop a Gaussian mixture model to design the color palette. Each component in the GMM represents a type of color in the color palette. The task of color quantization is to group pixels into different component. Experimental results show that our quantization method can obtain better results than other methods.

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