{"id":15046,"date":"2022-06-16T20:17:40","date_gmt":"2022-06-17T00:17:40","guid":{"rendered":"https:\/\/carleton.ca\/scs\/?page_id=15046"},"modified":"2022-06-16T20:17:40","modified_gmt":"2022-06-17T00:17:40","slug":"tr-208-fast-learning-automaton-based-image-examination-and-retrieval","status":"publish","type":"page","link":"https:\/\/carleton.ca\/scs\/research\/scs-technical-reports\/technical-reports-1992\/tr-208-fast-learning-automaton-based-image-examination-and-retrieval\/","title":{"rendered":"TR-208: Fast Learning Automaton-Based Image Examination and Retrieval"},"content":{"rendered":"

Carleton University
\nTechnical Report<\/a> TR-208<\/strong>
\nJune 1992<\/p>\n

Fast Learning Automaton-Based Image Examination and Retrieval<\/h2>\n
B. John Oommen & Chris Fothergill<\/div>\n
\n

Abstract<\/h3>\n<\/div>\n

In this paper we study the Image Examination and Retrieval Problem (IERP). Consider the scenario in which a user wants to browse through a database of images so as to retrieve a particular image which he is interested in. Rather than specifying the target image textually, we instead pennit the user to access his image by using his subjective discrimination of how it resembles other images that are presented to him by the system. The IERP is not merely viewed as one involving recognition or classification, but instead as one that falls in the domain of classifying and partitioning the set of images in tenns of their “visual” resemblances. In the process, we intend to not merely find images that match other images, but, in fact, to group all similar images together so that subsequent searches will be enhanced. The intelligent partitioning of the image database is done adaptively on the basis of the statistical properties of the user’s query patterns. This is achieved using learning automata and does not involve the evaluation of any statistics.<\/p>\n

<\/div>\n

TR-208.pdf<\/a><\/p>\n