Adaptive image segmentation using a genetic algorithm

Bhanu, Bir; Lee, Sungkee; Jia, Mingxing

doi:10.1109/21.478444

Cited by 206 publications

(104 citation statements)

References 23 publications

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“…A user typically needs to digitize or provide examples of desired segmentation results. Such an approach has attracted research attention in the imaging disciplines in general [26,28,[31][32][33][34][35] and also more specifically in the context of remote sensing image analysis [27,29,36]. It is a feasible strategy if a scene contains numerous "similar" elements that are of interest, common in many mapping tasks.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Segmentation is, generally computationally expensive, resulting in computationally expensive fitness evaluations. Searching the parameter space efficiently was a major driver for the development of this method [26]. Metaheuristics, which are stochastic population based search methods, are well suited and studied in the context of this general approach [26,29,42,43], commonly leading to higher quality fitness scores in less search time compared to more general search strategies.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Searching the parameter space efficiently was a major driver for the development of this method [26]. Metaheuristics, which are stochastic population based search methods, are well suited and studied in the context of this general approach [26,29,42,43], commonly leading to higher quality fitness scores in less search time compared to more general search strategies. Such a general approach may also be used to compare segmentation algorithms for a given task, or purely to test the general feasibility of a given algorithm for a given task.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Reference segment geometric properties are most commonly queried to drive the search process in sample supervised segment generation approaches [26][27][28]. Such reference segments also contain or encapsulate spectral data, which is implicitly provided.…”

Section: Exploiting Spectral Data Contained Within Provided Referencementioning

confidence: 99%

“…Various general approaches have been proposed to address the challenge of thematically accurate image segmentation and classification (semantic segmentation), including advocating rule-set or expert system's approaches within GEOBIA [1,14], the development of new domain specific segmentation algorithms [18], multi-scale image analysis [6,19,20], using context information or spatial relationships among segments [14,21,22], and hybridizing or interleaving classification and segmentation processes [23][24][25]. Another general approach addressing the problem of segmentation within GEOBIA casts the creation of thematically accurate image segments as a search or optimization problem [26][27][28][29]. In such an approach the parameters of a given segmentation algorithm is automatically tuned based on the provision of a limited amount of user provided reference segments.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation

Fourie

Schöepfer

2014

Remote Sensing

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Quality segment generation is a well-known challenge and research objective within Geographic Object-based Image Analysis (GEOBIA). Although methodological avenues within GEOBIA are diverse, segmentation commonly plays a central role in most approaches, influencing and being influenced by surrounding processes. A general approach using supervised quality measures, specifically user provided reference segments, suggest casting the parameters of a given segmentation algorithm as a multidimensional search problem. In such a sample supervised segment generation approach, spatial metrics observing the user provided reference segments may drive the search process. The search is commonly performed by metaheuristics. A novel sample supervised segment generation approach is presented in this work, where the spectral content of provided reference segments is queried. A one-class classification process using spectral information from inside the provided reference segments is used to generate a probability image, which in turn is employed to direct a hybridization of the original input imagery. Segmentation is performed on such a hybrid image. These processes are adjustable, interdependent and form a part of the search problem. Results are presented detailing the performances of four method variants compared to the generic sample supervised segment generation approach, under various conditions in terms of resultant segment quality, required computing time and search process characteristics. Multiple metrics, metaheuristics and segmentation algorithms are tested with this approach. Using the spectral data contained within user OPEN ACCESS Remote Sens. 2014, 6 11853 provided reference segments to tailor the output generally improves the results in the investigated problem contexts, but at the expense of additional required computing time.

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