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An image segmentation approach by improved watershed partition and DCT energy compaction has been proposed in this paper. The proposed energy compaction, which expresses the local texture of an image area, is derived by exploiting the discrete cosine transform. The algorithm is a hybrid segmentation technique which is composed of three stages. First, the watershed transform is utilized by preprocessing techniques: edge detection and marker in order to partition the image into several small disjoint patches, while the three features: region size, mean and variance are used to calculate region energy for combination. Then in the second merging stage, the DCT transform is used for energy compaction which is a criterion for texture comparison and region merging. Finally the image can be segmented into several partitions. The obtained results show good segmentation robustness and efficiency, when compared to other state of the art image segmentation algorithms.

An image segmentation approach by improved watershed partition and DCT energy compaction has been proposed in this paper. The proposed energy compaction, which expresses the local texture of an image area, is derived by exploiting the discrete cosine transform. The algorithm is a hybrid segmentation technique which is composed of three stages. First, the watershed transform is utilized by preprocessing techniques: edge detection and marker in order to partition the image in to several small disjoint patches, while the region size, mean and variance features are used to calculate region cost for combination. Then in the second merging stage the DCT transform is used for energy compaction which is a criterion for texture comparison and region merging. Finally the image can be segmented into several partitions. The experimental results show that the proposed approach achieved very good segmentation robustness and efficiency, when compared to other state of the art image segmentation algorithms and human segmentation results.

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