This paper presents an adaptable content-based image retrieval (CBIR) system developed using regularization theory, kernel-based machines, and Fisher information measure. The system consists of a retrieval subsystem that carries out similarity matching using image-dependant information, multiple mapping subsystems that adaptively modify the similarity measures, and a relevance feedback mechanism that incorporates user information. The adaptation process drives the retrieval error to zero in order to exactly meet either an existing multiclass classification model or the user high-level concepts using reference-model or relevance feedback learning, respectively. To facilitate the selection of the most informative query images during relevance feedback learning a new method based upon the Fisher information is introduced. Model-reference and relevance feedback learning mechanisms are thoroughly tested on a domain-specific image database that encompasses a wide range of underwater objects captured using an electro-optical sensor. Benchmarking results with two other relevance feedback learning methods are also provided.

This paper introduces a new connectionist network for certain domain-specific text-retrieval and search applications with expert end users. A new model reference adaptive system is proposed that involves three learning phases. Initial model-reference learning is first performed based upon an ensemble set of input-output of an initial reference model. Model-reference following is needed in dynamic environments where documents are added, deleted, or updated. Relevance feedback learning from multiple expert users then optimally maps the original query using either a score-based or a click-through selection process. The learning can be implemented, in regression or classification modes, using a three-layer network. The first layer is an adaptable layer that performs mapping from query domain to document space. The second and third layers perform document-to-term mapping, search/retrieval, and scoring tasks. The learning algorithms are thoroughly tested on a domain-specific text database that encompasses a wide range of Hewlett Packard (HP) products and for a large number of most commonly used single- and multiterm queries.

A new feature adaptation mechanism is proposed in this paper that captures the relevance feedback information provided by the expert users. This relevance information is retained for future usage and subsequently made available to other users. The search and retrieval processes are implemented through a two-layer connectionist network structure and the relevance feedback learning is incorporated by appropriately modifying the network structure. The developed algorithm is tested on an electro-optical imagery database collected from different underwater mine-like and non-mine-like objects.

This paper introduces a new connectionist network for large-scale text retrieval applications. A learning mechanism is proposed to optimally map the original query using relevance feedback from multiple expert users. The query mapping not only meets the requirements of the expert users but also preserves the positions and ranks of other relevant documents. An updating algorithm is also proposed to incorporate new documents (or delete the obsolete ones) into the system either one-by-one or in a batch mode without requiring to retrain the system. The algorithms are successfully tested on a large database and for a large number of most commonly used single-term or multi-terms queries.