Towards Path-based Semantic Dissimilarity Estimation for Scene Representation using Bottleneck Analysis

Abstract

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.