Image Analysis and Fast Indexing of Content
Carl Stahmer will present informally on National Endowment for the Humanities funded research to develop an open source, content based image recognition (CBIR) platform for traversal of archives of historical printed materials. In its current release, the software package, known as Arch-V (short for Archive Vision), utilizes a process that creates bags of features for each image based on extracted SURF feature points. These bags are then indexed for fast searching using SOLR/Lucene . Current development is focused on moving past the Bayesian bag of features approach to include consideration of next nearest neighbors as well as the identification of statistically significant feature sets for each image. The challenge is to find ways to represent significant feature clusters in a manner that still allows for fast indexing and on-the-fly addition of new images to the collection without the need for brute force matching to each image already in the collection. After a brief discussion of the underlying methods employed by the application, the group will discusses possible solutions to the data representation and analysis problems the project is currently confronting.