Published Paper Details:

In this paper, a general mining approach based on decision trees for segmenting image data is proposed. Pixel-wise image features are extracted and transformed into a database-like table that allows existing data mining algorithms to dig out useful information. Each tuple in the table has a feature descriptor consisting of a set of feature values for a given pixel along with its label. With the feature label, we can employ the decision tree to (1) discover relationship between the attributes of pixels and their target labels, (2) build a model for image processing by using the training data set. Both experiments and theoretical analysis are performed in our research. The results show that the proposed model is very efficient and effective for image mining and image segmentation. It can also be used to develop new image processing algorithms, refine existing algorithms, or act as an effective filter. The MIST algorithm generates randomly different addition chains for performing a particular exponentiation. This means that power attacks which require averaging over a number of exponentiation power traces becomes impossible. Moreover, attacks which are based on recognizing repeated use of the same pre-computed multipliers during an individual exponentiation are also infeasible. The algorithm is particularly well suited to cryptographic functions which depend on exponentiation and which are implemented in embedded systems such as smart cards. It is more efficient than the normal square-and-multiply algorithm and uses less memory than 4-ary exponentiation.