The binarized image is very critical to image visual feature extraction, especially shape feature, and the image binarization approaches have been attracted more attentions in the past decades. In this paper, the genetic algorithm is applied to optimizing the binarization threshold of the strip steel defect image. In order to evaluate our genetic algorithm based image binarization approach in terms of quantity, we propose the novel pooling based evaluation metric, motivated by information retrieval community, to avoid the lack of ground-truth binary image. Experimental results show that our genetic algorithm based binarization approach is effective and efficiency in the strip steel defect images and our quantitative evaluation metric on image binarization via pooling is also feasible and practical.
In order to detect sensitive images, a content-based detection method using image retrieval technology is proposed in this
paper. The method utilizes color correlogram and Gabor wavelet transformation to extract color and texture features, and
then employs integrated similarity approach to calculate similarity and affinity propagation clustering algorithm to group
the images in image database. In the end, the image is detected by the content-based image retrieval. Experiment results
show that the proposed method is better in detection accuracy ratio and retrieval efficiency than the traditional method
and using K-means clustering for image database.
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