Texture analysis by genetic programming

Song, Andy; Ciesielski, Victor

doi:10.1109/cec.2004.1331154

Cited by 26 publications

(18 citation statements)

References 4 publications

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“…Song et al (Song 2003;Song and Ciesielski 2004) use tree based GP for a series of object image texture classification problems, such as classification of bitmap patterns, Brodatz textures and mashing images. This work mainly focuses on the use of GP for binary classification problems.…”

Section: Gp Related Work To Object Recognitionmentioning

confidence: 99%

A linear structured approach and a refined fitness function in genetic programming for multi-class object classification

Zhang

Fogelberg

2007

Connection Science

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This paper describes an approach to the use of genetic programming (GP) to multi-class object recognition problems. Rather than using the standard tree structures to represent evolved classifier programs which only produce a single output value that must be further translated into a set of class labels, this approach uses a linear structure to represent evolved programs, which use multiple target registers each for a single class. The simple error rate fitness function is refined and a new fitness function is introduced to approximate the true feature space of an object recognition problem. This approach is examined and compared with the tree based GP on three data sets providing object recognition problems of increasing difficulty. The results show that this approach outperforms the standard tree based GP approach on all the tasks investigated here and that the programs evolved by this approach are easier to interpret. The investigation into the extra target registers and program length results in heuristic guidelines for initially setting system parameters.

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Section: Gp Related Work To Object Recognitionmentioning

confidence: 99%

A linear structured approach and a refined fitness function in genetic programming for multi-class object classification

Zhang

Fogelberg

2007

Connection Science

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“…The aim of the classification is to identify the target from candidates. Recently, evolutionary computation [15,16] has been used for image processing [17], and image processing is considered as a search in images.…”

Section: Image Processing For Human Detection and Object Recognitionmentioning

confidence: 99%

Tele-operated robots for monitoring based on sensor networks

Kubota

Ozawa

2008

2008 SICE Annual Conference

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In this paper, we discuss a robot vision system to perceive people and the environment around a teleoperated mobile robot. We developed a tele-operated mobile robot with a pan-tilt mechanism. The tele-operated robot is used as a moving sensor device for obtaining local information, while the environmental system is used as a fixed sensor device for obtaining global information. The robot can perceive the environment by receiving and integrating the global information from the environment. First, we apply steady-state genetic algorithms for extracting people and target objects from camera images. Next, we explain the Graphical User Interface (GUI) environment for the teleoperation and how to perform the cooperation between the tele-operated robot and the environmental system. Finally, we show experimental results of the proposed system.

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“…• Semi-elitist truncation (steps [3][4][5]: Similar to the elitist truncation, it firstly fills the new parent population P t+1 with solutions in F 1 , following by F 2 , F 3 , and so on. However, for each point (objective value) in the objective space, it randomly choose only one solution s ∈ G i j .…”

Section: Redundancyregulationsmentioning

confidence: 99%

“…Some research efforts have attempted to construct a part of object recognition programs, e.g., feature extractor [19], [20], [25], [42], [48], [50], edge detector [49] or interest point detector [22], [23], [26], while some researchers focus on construction of complete object recognition programs [5], [28], [34], [35], [38]. Also various image processing tasks have been studied, e.g., classification [19], [20], segmentation [31], [32], [35], [44], image retrieval [2], or texture analysis [4]. In this work, we focus on automatic construction of feature extraction programs (FEPs) for an image segmentation task.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Genetic Programming with Redundancy-Regulations for Automatic Construction of Image Feature Extractors

Watchareeruetai

Matsumoto

Takeuchi

et al. 2010

IEICE Trans. Inf. & Syst.

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SUMMARYWe propose a new multi-objective genetic programming (MOGP) for automatic construction of image feature extraction programs (FEPs). The proposed method was originated from a well known multiobjective evolutionary algorithm (MOEA), i.e., NSGA-II. The key differences are that redundancy-regulation mechanisms are applied in three main processes of the MOGP, i.e., population truncation, sampling, and offspring generation, to improve population diversity as well as convergence rate. Experimental results indicate that the proposed MOGP-based FEP construction system outperforms the two conventional MOEAs (i.e., NSGA-II and SPEA2) for a test problem. Moreover, we compared the programs constructed by the proposed MOGP with four human-designed object recognition programs. The results show that the constructed programs are better than two human-designed methods and are comparable with the other two human-designed methods for the test problem.

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Texture analysis by genetic programming

Cited by 26 publications

References 4 publications

A linear structured approach and a refined fitness function in genetic programming for multi-class object classification

A linear structured approach and a refined fitness function in genetic programming for multi-class object classification

Tele-operated robots for monitoring based on sensor networks

Multi-Objective Genetic Programming with Redundancy-Regulations for Automatic Construction of Image Feature Extractors

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