An Unsupervised Evaluation Measure of Image Segmentation: Application to Flower Image Segmentation

We present a new unsupervised metric for segmentation result evaluation based on Bayes classification error and image global contrast. First, we presented a comparative study between several unsupervised metrics in order to prove their limits. The qualitative study was performed to make a preliminary selection and to discard some measures unsuitable for evaluation of foreground/background segmentation on flower images. For the quantitative study, we proposed a validation protocol based on the vote technique and involving a comparison to the ground truth. Experiments were performed on Oxford flower dataset in order to select the best result between different segmentation results. The obtained result showed that our proposed metric gives the best results.

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