Hierarchical Clustering for Segmenting Fused Image Using Discrete Cosine Transform with Artificial Bee Colony Optimization

In this paper, a robust and improved image segmentation technique is proposed for segmentation of a fused image based on Discrete Cosine Transform (DCT) with Artificial Bee Colony Optimization (ABC) termed as DCTopti. It is very challenging task to perceive details information from a visual image due to variation of light, reflection, existence of shadow etc., while in case of infrared images, as the energy is tracked in the photometry, image can be acquired at night. Mainly image fusion is done to solve the object detection problem from the image, where the resultant fused image contains more information than the input images. K-means, an iterative method for separating a data into k number of groups has been opted to segment the fused images using Hierarchical algorithm. Structural Similarity Index Measure (SSIM) is used for comparing the quality of the resulting fused image using the proposed technique with other benchmark methods. The result of this proposed Hierarchical K-means Clustering (HKmC) method shows its robustness for segmenting the fused images.