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This paper presents a novel manifold learning approach that takes into account the intrinsic dataset geometry. The dataset structure is modeled in terms of a Correlation Graph and analyzed using Strongly Connected Components (SCCs). The proposed manifold learning approach defines a more effective distance among images, used to improve the effectiveness of image retrieval systems. Several experiments were conducted for different image retrieval tasks involving shape, color, and texturedoi:10.1109/icip.2014.7025379 dblp:conf/icip/PedronetteT14 fatcat:n2qg3cyxtzghznvhljllup43ai