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论文类型：期刊论文

发表时间：2019-12-01

发表刊物：IET COMPUTER VISION

收录刊物：EI、SCIE

卷号：13

期号：8

页面范围：691-699

ISSN号：1751-9632

关键字：object detection; image representation; image texture; path-based semantic dissimilarity estimation; scene representation; natural images; image elements; gradual variations; clutters; path-based bottleneck analysis method; semantic information; spatial continuity; feature consistency; double-S path; similar pattern; path-based bottleneck distance; image ranking; salient object detection

摘要：In natural images, it remains challenging to estimate dissimilarities between image elements for scene representation due to gradual variations of illuminations, textures or clutters. To tackle this problem, we utilise a path-based bottleneck analysis method that captures the semantic information between image elements to measure the dissimilarity. By integrating both the spatial continuity and feature consistency into the understanding of the semantic information, we detect the bottlenecks on the proposed double-S path to define the bottleneck distance, which demonstrates a favourable capability of grouping image elements that follow a similar pattern and separating different ones. In the experiments, the method is proved to be robust to noises and invariant to changing illumination and arbitrary scales in natural images. Tests on some challenging datasets validate the advantage of applying the path-based bottleneck distance in image ranking and salient object detection.