An Enhanced Louvain Based Image Segmentation Approach Using Color Properties and Histogram of Oriented Gradients

Segmentation techniques based on community detection algorithms generally have an over-segmentation problem. This paper then propose a new algorithm to agglomerate near homogeneous regions based on texture and color features. More specifically, our strategy relies on the use of a community detection on graphs algorithm (used as a clustering approach) where the over-segmentation problem is managed by merging similar regions in which the similarity is computed with Histogram of Oriented Gradients (named as HOG) and Mean and Standard deviation of color properties as features. In order to assess the performances of our proposed algorithm, we used three public datasets (Berkeley Segmentation Dataset (BSDS300 and BSDS500) and the Microsoft Research Cambridge Object Recognition Image Database (MSRC)). Our experiments show that the proposed method produces sizable segmentation and outperforms almost all the other methods from the literature, in terms of accuracy and comparative metrics scores.