Feature Representation Of The Image Based On Iteration And Fractal

This paper is concern that image representation methods and theory on the fractal structure and iterate procedure. Meanwhile it shows some new experimental plans and theoretical results. The main assignments are as follows:Study the Iterated Function System (IFS), study the covering properties when we draw the fractal graphs by the computers using the same iterated codes, give some meaningful experimental results and analyze some theoretical questions; When all the formula in the IFS are multiplied by a constant α, the behaviors of the system will be changed. The bifurcation diagram of a wavelet function is discussed. Its diagram shows specially that not only from period to chaotic state but also from the chaotic state to period. Above property of the IFS is used in the next parts of the paper.Study the fractal representation method, give the exchange method of Hilbert alignment and the structure of binary tree. Use the structure of binary tree and the weight center to index the image, define two kinds of distances, index and search the image database. This index methods have a good search effect to the images which are polluted, damaged and deformed. Analyze the fractal dimensions of the image set and the feature set, prove that the fractal dimensions of some image feature sets are less than that of the image set at the high dimension space, give the conclusion that the fractal dimension of the image sequence is far less than the space dimension.A new representation algorithm based on the iterated functions is applied to classify the football match images, the neighborhood and the learning factor of the SOF NN is used to enhance the robustness of the algorithm. In addition, a new scheme that employs the image as weight matrix is introduced to build a chaos neural network which has improved from the Hopfield neural network. In the network, each artificial neuron accumulates the stimulus effect which bases on the image weight matrix. So it is possible for the neural network to recall the embedded memories and associate the temporal image.Although the fractal and iterated representation methods are all in the elementary period, they has already shown the potential. At the end of the paper, some existed questions of the fractal and iterated representation and the work in the next step are discussed.