Superpixel-based object boundary gimmicking using optimized conditional random fields with random associations

Superpixel-based clustering approach gains attention as an efficient pre-processing step for image segmentation during the last decade. In general, color similarity and position of the pixels are used as similarity metrics for segmentation. In this proposed work, a two-level object segmentation framework is proposed, where the mid-level cues are meritoriously utilized for efficient object segmentation. In the first level, a superpixel-based object boundary gimmicking algorithm with improved distance measure is used to gimmick the cluster boundaries. To make the picture visualization as human vision friendly, the regular clusters are decomposed literally only at the required places to adhere with object boundaries. Spurious Clusters are identified and merged to remove the noisy superpixels. As a post-processing step, superpixel-based optimized conditional random field (CRF) with random association (SOCRA) algorithm is proposed for efficient segmentation. Here homogeneity of the superpixel is imposed by optimized CRF to augment the object clusters with random associations. CRFs are employed for its excellent capability to characterize the relationship among the random variables. Extensive performance evaluation shows the proposed boundary gimmicking approach significantly competes with the state-of-the-art methods. Results obtained with Berkeley segmentation dataset (BSDS300) are compared with state-of-the-art methods in terms of under segmentation error, boundary recall, and achievable segmentation accuracy.

Automated summary

The proposed two-level object segmentation framework utilizes a superpixel-based object boundary gimmicking algorithm and optimized conditional random field algorithm to achieve competitive results in terms of image visualization and performance evaluation.