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Fractals are more and more used in image analysis and the use of fractal dimensions has been much studied for image feature extraction and segmentation. Though fractal dimension is an essential property, the use of fractal dimensions is limited. First, using only fractal dimensions cannot completely characterize images. Secondly, there could exist different images having the same fractal dimensions. In the present paper, we introduce the s-dimension content as a new image feature and use it to better characterize images. The s-dimension content is calculated in help of the covering-blanket method. To more effectively extract the fractal features, we propose to change also the spatial window size for calculation of fractal characteristics in different scales. The inherent relation between the s- dimension content of fractals and the image features is studied. Experimental results for boundary between different regions are presented. We present also the application of s- dimension content to the detection of small objects on a noisy background, the experimental results are reported and are compared with those obtained by the use of fractal dimension. According to the experimental results, we see that for small object detection, the use of s-dimension content is much more efficient than that of the fractal dimension, which shows the importance of the s-dimension content for image analysis.
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