Segmentation of Natural Image Based on Colour Cohesion and Spatial Criteria

Segmenting a natural image is a complex task. Different semantic units may share similar visual features. On the other hand, such features can have variations even within a single unit. Proposed methodology relies on colour cohesion and spatial relationship between the components with cohesive colour. At first image colour space is clustered to map the original colour to a reduced set. Number of cluster is automatically detected by analyzing the intensity histograms of the colour channels. Based on the similarity in terms of mapped colours, pixels are grouped. Subsequently, the spatial inclusiveness criteria is considered to merge the pixels groups where one group is contained within another. Finally, an attempt is made to merge the adjacent regions based on colour gradient. Colour cohesion is conceptualized by the process of colour space clustering, grouping of pixels in terms of colour similarity and region merging based on colour gradient. The spatial criteria is taken into account in terms of spatial inclusiveness at intermediate level and adjacency at final stage. Proposed methodology is tested on Berkley segmentation dataset. Performance comparison with few other methodologies indicates the effectiveness of proposed methodology.