Segmentation of SAR images using similarity ratios for generating and clustering superpixels

Leloğlu, Uğur Murat
The superpixels are groups of similar neighbouring pixels which are perceptually meaningful and representationally efficient segments. Among those existing superpixel generating algorithms, simple linear iterative clustering (SLIC) seems to be one of the simplest ones. Its simplicity is due to adaption of a distance measure which is a linear combination of colour and spatial proximity. It is this measure that is modified using a similarity ratio. This modified measure is used to label the pixels within the search areas for generating the superpixels. This generation phase is further augmented with a clustering phase based on the same formulated similarity metric, which clusters the superpixels into larger segments. It has been demonstrated that this modified version performs better in terms of boundary recall and undersegmentation error, and is more robust to the speckle noise than the one in SLIC. Moreover, the clustered segments formed by superpixels generated by this approach has better boundary adherence than those formed by superpixels generated by SLIC.


Similarity Ratio Based Adaptive Mahalanobis Distance Algorithm to Generate SAR Superpixels
AKYİLMAZ, Emre; Leloğlu, Uğur Murat (2017-01-01)
Superpixel algorithms are aimed to partition an image into multiple similar sized segments based on similarity and proximity of pixels. In the heterogeneous regions, the boundaries of the objects should adhere well to the superpixels, and in the homogeneous parts, the pixels should be clustered so that compact superpixels are generated. Since speckle noise inherently exists in synthetic aperture radar (SAR) images their segmentation is considerably more difficult. In this article, the first contribution is ...
Image segmentation with unified region and boundary characteristics within recursive shortest spanning tree
Esen, E.; Alp, Y. K. (2007-06-13)
The lack of boundary information in region based image segmentation algorithms resulted in many hybrid methods that integrate the complementary information sources of region and boundary, in order to increase the segmentation performance. In compliance with this trend, we propose a novel method to unify the region and boundary characteristics within the canonical Recursive Shortest Spanning Tree algorithm. The main idea is to incorporate the boundary information in the distance metric of RSST with minor cha...
Segmentation Driven Object Detection with Fisher Vectors
Cinbiş, Ramazan Gökberk; Schmid, Cordelia (2013-01-01)
We present an object detection system based on the Fisher vector (FV) image representation computed over SIFT and color descriptors. For computational and storage efficiency, we use a recent segmentation-based method to generate class-independent object detection hypotheses, in combination with data compression techniques. Our main contribution is a method to produce tentative object segmentation masks to suppress background clutter in the features. Re-weighting the local image features based on these masks...
Object Segmentation in Multi-view Video via Color, Depth and Motion Cues
Cigla, Cevahir; Alatan, Abdullah Aydın (2009-01-01)
In the light of dense depth map estimation, motion estimation and object segmentation, the research on multi-view video (MVV) content has becoming increasingly popular due to its wide application areas in the near future. In this work, object segmentation problem is studied by additional cues due to depth and motion fields. Segmentation is achieved by modeling images as graphical models and performing popular Normalized Cuts method with some modifications. In the graphical models, each node is represented b...
Alignment of uncalibrated images for multi-view classification
Arık, Sercan Ömer; Vural, Elif; Frossard, Pascal (2011-12-29)
Efficient solutions for the classification of multi-view images can be built on graph-based algorithms when little information is known about the scene or cameras. Such methods typically require a pairwise similarity measure between images, where a common choice is the Euclidean distance. However, the accuracy of the Euclidean distance as a similarity measure is restricted to cases where images are captured from nearby viewpoints. In settings with large transformations and viewpoint changes, alignment of im...
Citation Formats
E. AKYİLMAZ and U. M. Leloğlu, “Segmentation of SAR images using similarity ratios for generating and clustering superpixels,” ELECTRONICS LETTERS, pp. 654–655, 2016, Accessed: 00, 2020. [Online]. Available: