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Friday, November 16, 2007
Real Time Face Detection
A new descriptor for real time face detection is now available in img(Rummager) 1.8 Beta. This descriptor uses 3 fuzzy systems in order to detect Skin Color, Eyes Possition and Face Shape. More details about the method will be added soon.
Thursday, November 15, 2007
Moments From Mallorca
Moments from the «IASTED International Conference on Artificial Intelligence and Soft Computing (ASC 2007)», August 29 to August 31, 2007, Palma De Mallorca, Spain.
Sunday, November 4, 2007
Particle Swarm Optimization
Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling.
PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles.
Each particle keeps track of its coordinates in the problem space which are associated with the best solution (fitness) it has achieved so far. (The fitness value is also stored.) This value is called pbest. Another "best" value that is tracked by the particle swarm optimizer is the best value, obtained so far by any particle in the neighbors of the particle. This location is called lbest. when a particle takes all the population as its topological neighbors, the best value is a global best and is called gbest.
The particle swarm optimization concept consists of, at each time step, changing the velocity of (accelerating) each particle toward its pbest and lbest locations (local version of PSO). Acceleration is weighted by a random term, with separate random numbers being generated for acceleration toward pbest and lbest locations.
In past several years, PSO has been successfully applied in many research and application areas. It is demonstrated that PSO gets better results in a faster, cheaper way compared with other methods.
Another reason that PSO is attractive is that there are few parameters to adjust. One version, with slight variations, works well in a wide variety of applications. Particle swarm optimization has been used for approaches that can be used across a wide range of applications, as well as for specific applications focused on a specific requirement.
http://www.swarmintelligence.org/index.php
PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles.
Each particle keeps track of its coordinates in the problem space which are associated with the best solution (fitness) it has achieved so far. (The fitness value is also stored.) This value is called pbest. Another "best" value that is tracked by the particle swarm optimizer is the best value, obtained so far by any particle in the neighbors of the particle. This location is called lbest. when a particle takes all the population as its topological neighbors, the best value is a global best and is called gbest.
The particle swarm optimization concept consists of, at each time step, changing the velocity of (accelerating) each particle toward its pbest and lbest locations (local version of PSO). Acceleration is weighted by a random term, with separate random numbers being generated for acceleration toward pbest and lbest locations.
In past several years, PSO has been successfully applied in many research and application areas. It is demonstrated that PSO gets better results in a faster, cheaper way compared with other methods.
Another reason that PSO is attractive is that there are few parameters to adjust. One version, with slight variations, works well in a wide variety of applications. Particle swarm optimization has been used for approaches that can be used across a wide range of applications, as well as for specific applications focused on a specific requirement.
http://www.swarmintelligence.org/index.php
A Hybrid Scheme for Fast and Accurate Image Retrieval based on Color Descriptors
This paper proposes a new image retrieval system, that uses only color features and it's based on a hybrid scheme which combines crisp and fuzzy techniques, in order to retrieve color based similar images. The system comprises 2 units. The first unit uses Binary Haar Wavelet Descrip tor in a histogram that has been proposed in MPEG-7. A new fuzzy-linking method of color histogram creation is also proposed, based on the HSV color space. The second unit provides this histogram and decides about the simi larity. The system is suitable for accurately retrieving im ages even in distortion cases such as deformations, noise and smoothing. It is tested on a large number of images selected from proprietary image data bases or randomly retrieved from popular search engines. The retrieval rate approximates 45 images (size of 250 X 250 pixels) per second, assuming no prior stored feature information in the searched image data bases. To evaluate the perform ance of the proposed system, objective measure called ANMRR is used.
From Proceeding (584) Artificial Intelligence and Soft Computing - 2007
http://www.actapress.com/PaperInfo.aspx?PaperID=31620
From Proceeding (584) Artificial Intelligence and Soft Computing - 2007
http://www.actapress.com/PaperInfo.aspx?PaperID=31620
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