Global optimization methods in content-based image retrieval

Content-based image retrieval involves a search throughout a database of stored images for the best match for the query image. The task is re-formulated as the global optimization problem of finding the correct mapping between the corresponding points of the query image and the database image. For 2-dimensional grayscale images, the quality of the match is evaluated as the difference between the pixel values in the area of the intersection of the two images: the minimum value of the difference indicates a potential match between the images, with the corresponding optimal values of the parameters defining the mapping. The stated problem is a nonlinear, multimodal global optimization problem. In general form, the mapping includes the rigid body transform and the local object deformation. If there is no prior information available about the images, the search space of potential solutions becomes so large that the brute force approach becomes intractable. The classical optimization techniques fail due to the presence of many local minima and the non-convex shape of the nonlinear function defining the difference between the images. The following stochastic optimization techniques are compared in the paper: parallel simulated annealing, multi-start, and hybrid evolutionary algorithm. The methods differ in the degree to which they utilize global and local search, and in the strategy of the global search. The comparison is presented for the grayscale images, with different initial settings.