Synthesis of interest point detectors through genetic programming

Trujillo, Leonardo; Olague, Gustavo

doi:10.1145/1143997.1144151

Cited by 69 publications

(62 citation statements)

References 15 publications

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“…Note that our feature extraction method is simple and could be improved by applying more accurate edge detection algorithms as described by, e.g., Fabijańska (2012), or by using a more sophisticated interest point detector, proposed, e.g., by Trujillo and Olague (2006). Each primitie is described by three scalars called hereafter attributes; these include two spatial coordinates of the edge fragment (x and y) and the local gradient orientation.…”

Section: Generative Visual Learningmentioning

confidence: 99%

Cross-task code reuse in genetic programming applied to visual learning

Jaśkowski¹,

Krawiec²,

Wieloch³

2014

International Journal of Applied Mathematics and Computer Science

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We propose a method that enables effective code reuse between evolutionary runs that solve a set of related visual learning tasks. We start with introducing a visual learning approach that uses genetic programming individuals to recognize objects. The process of recognition is generative, i.e., requires the learner to restore the shape of the processed object. This method is extended with a code reuse mechanism by introducing a crossbreeding operator that allows importing the genetic material from other evolutionary runs. In the experimental part, we compare the performance of the extended approach to the basic method on a real-world task of handwritten character recognition, and conclude that code reuse leads to better results in terms of fitness and recognition accuracy. Detailed analysis of the crossbred genetic material shows also that code reuse is most profitable when the recognized objects exhibit visual similarity.

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Section: Generative Visual Learningmentioning

confidence: 99%

Cross-task code reuse in genetic programming applied to visual learning

Jaśkowski¹,

Krawiec²,

Wieloch³

2014

International Journal of Applied Mathematics and Computer Science

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“…The first work in this area employed GP to evolve an operator for detecting interest points [4]. Trujillo and Olague [31] have also used GP to generate feature extractors for computer vision applications. In addition, a GP-based detector was proposed by Howard et al [7] for detecting ship wakes in synthetic aperture radar (SAR) images.…”

Section: Related Workmentioning

confidence: 99%

Genetic Programming-Evolved Spatio-Temporal Descriptor for Human Action Recognition

Liu¹,

Shao²,

Rockett³

2012

Procedings of the British Machine Vision Conference 2012

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The potential value of human action recognition has led to it becoming one of the most active research subjects in computer vision. In this paper, we propose a novel method to automatically generate low-level spatio-temporal descriptors showing good performance, for high-level human-action recognition tasks. We address this as an optimization problem using genetic programming (GP), an evolutionary method, which produces the descriptor by combining a set of primitive 3D operators. As far as we are aware, this is the first report of using GP for evolving spatio-temporal descriptors for action recognition. In our evolutionary architecture, the average cross-validation classification error calculated using the support-vector machine (SVM) classifier is used as the GP fitness function. We run GP on a mixed dataset combining the KTH and the Weizmann datasets to obtain a promising feature-descriptor solution for action recognition. To demonstrate generalizability, the best descriptor generated so far by GP has also been tested on the IXMAS dataset leading to better accuracies compared with some previous hand-crafted descriptors.

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“…Johnson et al [11] have used Genetic Programming to evolved visual routines. Current research focuses on the evolution of low-level detectors [22] and object recognition [14]. A taxonomic tutorial on the field of evolutionary computer vision is given by Cagnoni [2].…”

Section: Evolutionary Computer Visionmentioning

confidence: 99%

Evolving Object Detectors with a GPU Accelerated Vision System

Ebner

2010

Evolvable Systems: From Biology to Hardware

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Abstract. Using GPU processing, it is now possible to develop an evolutionary vision system working at interactive frame rates. Our system uses motion as an important cue to evolve detectors which are able to detect an object when this cue is not available. Object detectors consist of a series of high level operators which are applied to the input image. A matrix of low level point operators are used to recombine the output of the high level operators. With this contribution, we investigate, which image processing operators are most useful for object detection. It was found that the set of image processing operators could be considerably reduced without reducing recognition performance. Reducing the set of operators lead to an increase in speedup compared to a standard CPU implementation. MotivationIn the field of evolutionary computer vision, evolutionary algorithms are used to search for optimal or approximately optimal solutions for computer vision problems [2]. A programmer developing a computer vision algorithm needs to decide in what sequence well known image processing operators have to be arranged to obtain a desired result. In evolutionary computer vision, Genetic Programming [1,13] is used to arrange different image processing operators to obtain a particular output or to perform a given task such as object recognition. This approach is particularly interesting for problems for which the solution is not readily apparent to those skilled in the art. Unfortunately, most experiments in evolutionary computer vision require enormous computational resources because multiple algorithms have to be evaluated over several generations to find an appropriate solution. However, the graphics processing unit (GPU) of a PC can be used for speeding up image processing tasks [7,8]. The GPU is ideal for speeding up image processing as the same operation usually needs to be computed for each image pixel.We have created a GPU accelerated evolutionary image processing system which is able to learn how to detect a user-specified object in an image [3,4].

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Synthesis of interest point detectors through genetic programming

Cited by 69 publications

References 15 publications

Cross-task code reuse in genetic programming applied to visual learning

Cross-task code reuse in genetic programming applied to visual learning

Genetic Programming-Evolved Spatio-Temporal Descriptor for Human Action Recognition

Evolving Object Detectors with a GPU Accelerated Vision System

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