A Region-Based Image Segmentation by Watershed Partition and DCT Energy Compaction

Abstract

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.

Segmentation for High-Resolution Optical Remote Sensing Imagery Using Improved Quadtree and Region Adjacency Graph Technique

“…See case (A) in Figure 7b. Then add e to edge set E, and add the 2 nodes to vertex set V. In the example in Figure 7a, the satisfied edges are w (9,10), w (9,11), w (9,12), and w (11,12); Case B: The states of two nodes connected by e are all VN, and they are descendants of the same ancestor. See case (B) in Figure 7b.…”

“…Reform it to , which is a connection between the ancestor of VN node and the original RL node. Then add to edge set E, and the two nodes after reformation to vertex set V. In the example shown in Figure 7a, the satisfied edges include: w(1,9) reformed from w (6,9), w (1,11) reformed from w (8,11), w (11,4) reformed from w (11,17), and w (12,4) reformed from w (12,18).…”

“…The algorithm treats each local minimum and surrounding area as a basin for catchments, and the basin watershed as a boundary of image segment. Based on this theory, a deeper analysis can be discussed on merging technique [11,12]. Multi-Resolution (MR) segmentation algorithm considers both spectrum and geometry factors as region growing criteria [13], which is implemented with the commercial software eCognition [14].…”

Ideology of Image Segmentation and Methods Thereof

“…In this algorithm, an analogy is made between image and its constituent objects with watershed (ridge) and various catchment basins in it flooded with water. The output of this algorithm is the input image into its different catchment basins signifying objects in image and each basin is characterized by a unique label [16].…”

Split and Merge Based Quantitative Approach to Select Filter Combination for Image Segmentation

“…All these algorithms work on the use of any of the three main criteria: the homogeneity within a segment, separation from adjacent segments and shape homogeneity. Typically, the segmentation algorithms can be grouped into three major categories on the basis of their segment formation properties, namely Threshold Based Segmentation [11,22], Boundary based Segmentation [9,16] and Region Based Segmentation [10].…”