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Images to test different segmentation algorithms

asked 2015-03-02 09:55:11 -0500

Sowmya gravatar image


I want to know the disadvantages and advantages of different segmentation algorithms like

Otsu's multiple thresholds, kmeans, Expectation maximization, mean shift algorithm, normalized cuts etc.

Can anyone share me the source where I can find all the information about these segmentations. And also I want proper images to demonstrate the advantages and disadvantages of one algorithm with another

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answered 2015-03-03 06:07:29 -0500

Common approach: Berkeley Segmentation Dataset (BSDS300) [1] using the precision-recall framework introduced in [2]. Taken from [3].

The BSDS300 consists of 200 training and 100 test images, each with multiple ground-truth segmentations.

An extension of the BSDS300 is created (BSDS500) [3]. It is is an extension of the BSDS300, where the original 300 images are used for training / validation and 200 fresh images, together with human annotations, are added for testing. Each image was segmented by five different subjects on average. Performance is evaluated by measuring Precision / Recall on detected boundaries and three additional region-based metrics.

Link to download BSDS500 :

See also this publication: 'Benchmarking Image Segmentation Algorithms' [4] And these links:

[1] D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” ICCV, 2001. (

[2] D. Martin, C. Fowlkes, and J. Malik, “Learning to detect natural image boundaries using local brightness, color and texture cues,” PAMI, 2004. (

[3] Arbelaez, P., Maire, M., Fowlkes, C., & Malik, J. (2011). Contour detection and hierarchical image segmentation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(5), 898-916. (

[4] Estrada, F. J., & Jepson, A. D. (2009). Benchmarking image segmentation algorithms. International Journal of Computer Vision, 85(2), 167-181. (

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Asked: 2015-03-02 09:55:11 -0500

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