Content-based retrieval of distorted 3D images with a hybrid evolutionary algorithm

Content-based image retrieval in many cases involves performing a direct mapping operation between a query image and images stored in a database. Preliminary results are discussed on using image mapping through unsupervised learning, in the form of a Hybrid evolutionary algorithm (HEA), in a search for 3-dimensional objects that can be present in the database images. Content-based retrieval problem is formulated as the optimization problem of finding the proper mapping between the stored and the query images. The paper proposes an extension of the HEA-based method of the 2-dimensional image mapping to the 3-dimensional case. A set of image transformations is sought such that each transformation is applied to a different section of the image subject to mapping. The sought image transformation becomes a piece-wise approximation of the actual 3-D transformation of the object. The 2-D optimization problem of finding a parameter vector minimizing the difference between the images turns into a multi-objective optimization problem of finding a set of feasible parameter vectors that minimize the differences between the sections of the compared images. The search for a proper set of image transformations is conducted in a feature space formed by image local response, as opposed to a pixel-wise comparison of the actual images in the 2-D case. Using image response allows to reduce the computational cost of the search by applying thresholding techniques and a piece-wise approximation of the response matrix. The difference between the images is evaluated in the response space by minimizing the distance between the two-dimensional central moments of the image responses.