Object and Concept Recognition for Content-Based Image Retrieval

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The goal of this research is to develop the necessary methodology for automated recognition of generic object and concept classes (such as buildings, cars, boats, and trees) in digital images in order to substantially improve the process of content-based image retrieval, which has relied mainly on low-level color and texture features for matching queries to database images. The approach has three major aspects: (1) to design new high-level image features including cluster features that group together lower-level features and relationship features that capture spatial relationships among them; (2) to develop a unified representation that can express a large variety of both low- and high-level features in a form that can be used by learning systems; and (3) to automate the development of recognizers for object and concept classes through the use of a hierarchical, multiple classifier methodology. The resulting techniques are being evaluated on several different large image databases, including commercial databases whose images are grouped into broad classes and a ground-truth database that provides a list of the objects in each image. The results of this work will be a new generic object and concept recognition paradigm that can immediately be applied to automated or semi-automated indexing of large image databases. The methodology will help to bridge the gap between the high-level needs of users of image retrieval systems and the low-level features typically extracted from an image. The generic object class recognition algorithms we develop will begin a new era of object recognition research, leaving the geometric domain and entering the conceptual domain.