There has been tremendous progress in the areas of image processing (input: images, output: images) and computer graphics (input: numbers, output: images). Unfortunately, progress in image analysis (input: images, output: numbers) has been much slower. In this paper, we first briefly introduce the ideas of image analysis using class 2 dynamical systems and image analysis using class 3 dynamical systems. Then we compare these two approaches. The similarities of the two schemes are: (1) both methods use the ideas of storing information in stable configurations of dynamical systems and compress a huge image into a tiny vector that catches the characteristics of the image efficiently. Both methods are very general and they can be generalized to any type of images. (2) The mapping from an image to numbers is determined by the mapping from a specification of a dynamical system to the corresponding attractor it contains. (3) All image analysis algorithms (quasi-enumerative search, random enumerative search, local search, simulated annealing, and greedy) are similar. (4) Technically, local minima approaches (deterministic or probabilistic) remain to be the only best approach. All the limits, including converge to wrong minima and hence increase the error of analysis, apply for both approaches. (5) Theoretically, the unsolved problems (information capacity) are similar.