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Associative methods for content-based image ranking by semantics are attractive due to the similarity of generated models to human models of understanding. Although they tend to return results that are better understood by image analysts, the induction of these models is difficult to build due to factors that affect training complexity, such as coexistence of visual patterns in same images, over-fitting or under-fitting and semantic representation differences among image analysts. This articledoi:10.3390/ijgi2020531 fatcat:hfdhuwauwvfgxpfwafqjpxtzvy