An original image retrieval framework is proposed and developed. Trying to achieve the semantic retrieval, a novel cognitive model - feature element constructional model is proposed. With its hierachical constructional structure and bias competition mechanism, the new model provides great power for semantic retrieval. Two types of retrieval mode are presented in the new system, which both try to analysis the semantic concept in the query image or semantic command. Then matching from the object to the feature element is carried out to obtain the final result, and our understanding of retrieval “to provide the way of approaching the accurate result” is also embodied.
Traditional systems of image retrieval work as black boxes. The concrete process and result data or coefficients are not the real care point. Thus it brings the problem: these systems cannot fulfill general semantic application. To overcome the problem we turn to psychology and neuroscience to study the cognition mechanism of human brain. Based on the analysis of experiments and evidences, a new hypothesis - element presence theory is proposed to explain the truth of the whole visual cognition. As its basic level that deals with low-level feature data from camera or retina, feature element theory is illustrated in details. Besides, the evaluation on feature elements is discussed and the illustration on feature element theory based image retrieval system is also given.
An original image retrieval framework is proposed and developed. Different from the popular systems of retrieval, we break features into feature elements - FEs which have meaningful visual sense instead of combining them to get semantic meanings. These feature elements are evaluated according to the subjective perception of human beings. As result, three classes of feature elements are obtained as important FEs, extend FEs and trivial FEs. Each class of feature elements is organized to form the FE Data Set. Then the retrieval process is turned into searching the feature elements in corresponding sets. Interactive function is also built in. With association feedback, the associated feature elements of both user interest and the given retrieval result are detected and analyzed. Thus, the system can grasp the user's target more accurately. Even if the user switches to other retrieval interest, the system can also trace it by the associated part of the feature elements. As the whole approach is based on the instinctive perception, fine effect is reached.
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