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The paper presents a new approach for feature representation using semantic line groupings in an image. The algorithm uses the hypothesis in line with Gestalt laws of proximity that as a baseline in an image, semantic structures are formed by line segments placed in close proximity to each other. The algorithm uses line segments in an image to form semantic groups based on a minimum distance threshold. The semantic line groupings are differentiated from each other by the number of group members and their geometrical properties represented as histograms. The results are analyzed using different similarity measures to understand the strengths and weaknesses of the grouping approach and those of the similarity measures.