Introduction
Image segmentation plays a crucial role in computer vision and image processing tasks, allowing us to divide an image into distinct regions or objects with similar characteristics. In this blog post, we will explore clustering-based image segmentation techniques, specifically focusing on K-means clustering, clustering by mode-seeking, and graph partitioning (normalized cut) on color values. We will provide a detailed overview of the process and showcase how to implement these techniques using existing functions in popular Python libraries like scikit-image and scikit-learn.
Overview of Clustering-based Image Segmentation:
Define the feature space: The feature space represents the features used to describe each pixel in the image. This can include color values, texture, shape, and other relevant attributes.
Choose a clustering algorithm: Select a suitable clustering algorithm to group pixels based on their similarity in the feature space. Common choices include k-means, fuzzy c-means, and hierarchical clustering.
Determine the number of clusters: Decide on the number of clusters based on image characteristics and desired segmentation level. More clusters result in finer segmentation but can lead to over-segmentation.
Assign each pixel to a cluster: Assign each pixel to the cluster it is most similar to, based on the chosen feature space.
Post-processing: Apply post-processing techniques like smoothing and merging to enhance the segmented image's quality and remove noise or artifacts.
Problem Statement
Our project aims to implement a code that performs image segmentation using clustering methods, specifically K-means clustering, clustering by mode-seeking, and graph partitioning (normalized cut) on color values. We will demonstrate the code using an image of flowers as our dataset.
Dataset: The dataset consists of an image of flowers, which we will use for segmentation purposes.
Dataset
It consists of an image of flowers.
Tasks
An image can be segmented into a set of similar regions, which is often done using clustering methods. Write a code that segments the input image based on i) K-means clustering, ii) clustering by mode-seeking, and iii) graph partitioning (normalized cut) on the color values. You can use any existing functions. In particular, functions in segmentation and graph modules of scikit-image package and/or cluster module of scikit-learn package would be useful.
Use either the MeanShift class in sklearn.cluster or quickshift function in skimage.segmentation module.
Run you code on “flower1.jpg” image using two different color spaces: RGB and Lab, where “L” is the lightness, “a” and “b” represent color values. Some of the existing functions for image segmentation have “convert2lab” parameters. If you can’t convert the color space using parameter setting, you can use rgb2lab function in skimage package to convert an RGB image to Lab color space. For K-means clustering, use K = 5.
Display the segmentation results along with the input image as shown below. In the segmented image, represent each region by the average color of pixels in that region (cluster)
Set the title of the resulting segmented image as “{CLUSTERING_METHOD}_{RGB/Lab}.jpg”, where CLUSTERING_METHOD = KM, MS, or Ncut for K-means, mode-seeking, and normalized cut, respectively.
Clustering-based image segmentation techniques offer a powerful approach to partitioning images into distinct regions or objects. By utilizing clustering algorithms and appropriate feature spaces, we can achieve accurate segmentation, enabling applications such as object recognition, scene understanding, and image editing. With the help of scikit-image and scikit-learn libraries, implementing these techniques becomes accessible and efficient.
If you have any questions or need assistance with the code implementation, feel free to ask in the comments section below. We are here to help you in your journey towards mastering clustering-based image segmentation techniques.
Screenshots:
If you need implementation for the above problem or any of its variants, feel free to contact us.
