@inproceedings{frosini_persistent_2011, abstract = {In content-based image retrieval a major problem is the presence of noisy shapes. It is well known that persistent Betti numbers are a shape descriptor that admits a dissimilarity distance, the matching distance, stable under continuous shape deformations. In this paper we focus on the problem of dealing with noise that changes the topology of the studied objects. We present a general method to turn persistent Betti numbers into stable descriptors also in the presence of topological changes. Retrieval tests on the Kimia-99 database show the effectiveness of the method.}, author = {Frosini, Patrizio and Landi, Claudia}, booktitle = {Computer Analysis of Images and Patterns}, date = {2011}, doi = {10.1007/978-3-642-23672-3_36}, editor = {Real, Pedro and Diaz-Pernil, Daniel and Molina-Abril, Helena and Berciano, Ainhoa and Kropatsch, Walter}, isbn = {978-3-642-23672-3}, keywords = {1 - Image retrival, 2 - Hausdorff distance, 2 - Multidimensional persistent homology, 2 - Persistent Betti numbers, 2 - Persistent homology, 2 - Symmetric difference distance, 3 - Shapes, 3 - images:2d}, langid = {english}, location = {Berlin, Heidelberg}, pages = {294--301}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Persistent Betti Numbers for a Noise Tolerant Shape-Based Approach to Image Retrieval} }