Abstract:

A weighted-sparse subspace clustering method with multi-feature fusion is proposed for image segmentation. Integration of multiple features can reliably describe the characteristics of various objects in natural images, thus can improve the accuracy and reliability of segmentation. The weighted-sparse measure is defined by introducing weights in the 1-norm of vectors. The weight is inversely proportional to the similarity between data, therefore the weighted 1-norm penalty on the linear representation coefficients tends to force similar data be involved while dissimilar data uninvolved in the linear representation of a datum. The resulted representation can overcome the drawbacks of 1-norm penalty that the presentation coefficients are usually over sparse and not robust for highly correlated data. Experimental results and objective assessment indexes show that the proposed method can effectively segment natural images with good visual consistency.