Matlab Image Segmentation using K-means Clustering and Autoencoder

Author : Waqas Javaid

Abstract:

Image segmentation is a crucial task in computer vision, involving the division of an image into its constituent parts or objects. This article presents a hybrid approach to image segmentation, combining K-means clustering and Autoencoder techniques. The K-means algorithm is used to initially segment the image, while an Autoencoder is trained to learn features from the image patches. The K-means clustering algorithm has been widely used for image segmentation [1]. The trained Autoencoder is then used to extract features, which are clustered using K-means to produce the final segmentation. The proposed method is evaluated on the peppers image, demonstrating improved accuracy and robustness to noise compared to traditional K-means segmentation. The Autoencoder’s ability to learn complex features enhances segmentation performance. Experimental results show the effectiveness of the proposed approach, outperforming K-means in terms of accuracy. The method can be applied to various image segmentation tasks, such as object detection and image annotation. Deep learning techniques, such as Autoencoders, have been shown to be effective in image segmentation tasks [2]. Future work includes exploring advanced Autoencoder architectures and applying the method to other tasks. The proposed approach offers a promising solution for image segmentation, leveraging the strengths of both K-means and Autoencoder techniques. The article discusses the methodology, results, and future directions for this research.

1.    Introduction:

Image segmentation is a fundamental task in computer vision, involving the division of an image into its constituent parts or objects. This process is essential for various applications, including object detection, image annotation, and image analysis. Traditional image segmentation techniques, such as thresholding and edge detection, are often limited by their inability to handle complex images. Deep learning techniques, such as Autoencoders, have been shown to be effective in image segmentation tasks [3]. K-means clustering is a widely used technique for image segmentation, which partitions the image into K clusters based on intensity values. However, K-means has limitations, including sensitivity to noise and outliers. To address these limitations, researchers have explored the use of deep learning techniques, such as Autoencoders, for image segmentation. Autoencoders are neural networks that learn to compress and reconstruct images, capturing complex features and patterns. By combining K-means with Autoencoders, we can leverage the strengths of both techniques to achieve improved image segmentation results.

This article presents a hybrid approach to image segmentation, using K-means clustering and Autoencoders. The proposed method is evaluated on the peppers image, demonstrating improved accuracy and robustness to noise. The Autoencoder’s ability to learn complex features enhances segmentation performance. The combination of K-means clustering and Autoencoders has been proposed for image segmentation [4]. The article discusses the methodology, results, and future directions for this research. The proposed approach offers a promising solution for image segmentation, leveraging the strengths of both K-means and Autoencoder techniques. Image segmentation is a crucial step in many computer vision applications. The goal of image segmentation is to divide the image into meaningful regions. K-means is a simple and efficient clustering algorithm. Autoencoders are powerful tools for feature learning. The combination of K-means and Autoencoders provides a robust approach to image segmentation. This approach can be applied to various image segmentation tasks. The peppers image is used as a test case for the proposed method. The results demonstrate the effectiveness of the proposed approach. The proposed method outperforms traditional K-means segmentation. The combination of K-means clustering and Autoencoders has been proposed for image segmentation [5]. The Autoencoder’s ability to learn complex features is key to improved performance. Future work includes exploring advanced Autoencoder architectures. The proposed approach has potential applications in object detection and image annotation. Image segmentation is an active area of research. The use of deep learning techniques is becoming increasingly popular. The proposed approach offers a promising direction for future research.

1.1    Image Segmentation:

Image segmentation is a fundamental task in computer vision, involving the division of an image into its constituent parts or objects. This process is essential for various applications, including object detection, image annotation, and image analysis. Traditional image segmentation techniques, such as thresholding and edge detection, are often limited by their inability to handle complex images. Autoencoders can learn complex features from images, which can improve the performance of K-means clustering [6]. K-means clustering is a widely used technique for image segmentation, which partitions the image into K clusters based on intensity values. However, K-means has limitations, including sensitivity to noise and outliers. To address these limitations, researchers have explored the use of deep learning techniques, such as Autoencoders, for image segmentation. Autoencoders are neural networks that learn to compress and reconstruct images, capturing complex features and patterns. By combining K-means with Autoencoders, we can leverage the strengths of both techniques to achieve improved image segmentation results. This article presents a hybrid approach to image segmentation, using K-means clustering and Autoencoders. The proposed method is evaluated on the peppers image, demonstrating improved accuracy and robustness to noise.

1.2    Limitations of Traditional Image Segmentation Techniques:

Traditional image segmentation techniques, such as thresholding and edge detection, are often limited by their inability to handle complex images. These techniques rely on simple features, such as intensity values or gradient information, which are not sufficient to capture the complexity of real-world images. Autoencoders can learn complex features from images, which can improve the performance of K-means clustering [7]. As a result, these techniques often produce poor segmentation results, especially in images with varying lighting conditions or complex textures. K-means clustering is a widely used technique for image segmentation, which partitions the image into K clusters based on intensity values. However, K-means has limitations, including sensitivity to noise and outliers. To address these limitations, researchers have explored the use of deep learning techniques, such as Autoencoders, for image segmentation. Autoencoders are neural networks that learn to compress and reconstruct images, capturing complex features and patterns. By combining K-means with Autoencoders, we can leverage the strengths of both techniques to achieve improved image segmentation results. The proposed method is evaluated on the peppers image, demonstrating improved accuracy and robustness to noise. The Autoencoder’s ability to learn complex features enhances segmentation performance.

1.3    K-means Clustering:

K-means clustering is a widely used technique for image segmentation, which partitions the image into K clusters based on intensity values. The algorithm works by initializing K cluster centers randomly and then assigning each pixel to the closest cluster center. The cluster centers are then updated by taking the mean of all pixels assigned to each cluster. The use of convolutional Autoencoders has been shown to be effective in image segmentation tasks [8]. This process is repeated until convergence, resulting in a segmented image. However, K-means has limitations, including sensitivity to noise and outliers. To address these limitations, researchers have explored the use of deep learning techniques, such as Autoencoders, for image segmentation. Autoencoders are neural networks that learn to compress and reconstruct images, capturing complex features and patterns. By combining K-means with Autoencoders, we can leverage the strengths of both techniques to achieve improved image segmentation results. The proposed method is evaluated on the peppers image, demonstrating improved accuracy and robustness to noise. The Autoencoder’s ability to learn complex features enhances segmentation performance.

1.4    Autoencoders:

Autoencoders are neural networks that learn to compress and reconstruct images, capturing complex features and patterns. The encoder maps the input image to a lower-dimensional representation, while the decoder maps the representation back to the original image. The Autoencoder is trained using a reconstruction loss function, which measures the difference between the input image and the reconstructed image. Once trained, the Autoencoder can be used to extract features from images, which can be used for image segmentation. By combining K-means with Autoencoders, we can leverage the strengths of both techniques to achieve improved image segmentation results. 6. The attention mechanism can be used to improve the performance of Autoencoders in image segmentation [9]. The proposed method is evaluated on the peppers image, demonstrating improved accuracy and robustness to noise. The Autoencoder’s ability to learn complex features enhances segmentation performance. Autoencoders have been widely used for image segmentation tasks, including object detection and image annotation. The proposed method offers a promising solution for image segmentation, leveraging the strengths of both K-means and Autoencoder techniques.

2.    Problem Statement:

Image segmentation is a fundamental task in computer vision that involves dividing an image into its constituent parts or objects. Traditional image segmentation techniques, such as thresholding and edge detection, are often limited by their inability to handle complex images. K-means clustering is a widely used technique for image segmentation, but it has limitations, including sensitivity to noise and outliers. The goal of this research is to develop a robust image segmentation method that can handle complex images and improve segmentation accuracy. To achieve this goal, we propose a hybrid approach that combines K-means clustering with Autoencoders. The Autoencoder is used to learn complex features from the image, which are then used to improve the segmentation performance of K-means. The proposed method is evaluated on the peppers image, demonstrating improved accuracy and robustness to noise. The Autoencoder’s ability to learn complex features enhances segmentation performance. The proposed method offers a promising solution for image segmentation, leveraging the strengths of both K-means and Autoencoder techniques. The research aims to contribute to the development of more accurate and robust image segmentation methods.

3.    Mathematical Approach:

The proposed segmentation method combines classical K-means clustering with learned nonlinear features extracted using an Autoencoder. Given an input grayscale image , the K-means algorithm first partitions pixel intensities by minimizing

Where, (xi) is the intensity of pixel (i), and (µk) is the centroid of cluster (k). To obtain richer representations, the image is divided into overlapping patches, each reshaped as a vector

. An Autoencoder learns a latent representation (z) through the mappings (z=f(Wx+b)) and , trained by minimizing the reconstruction loss:

 Once trained, the encoded features are clustered using K-means by minimizing:

where (Cik) denotes the binary assignment of feature (zi) to cluster (k). Each patch cluster label is then projected back to its spatial location, and overlapping regions are averaged to yield a smooth segmentation map. Finally, each pixel is assigned to the cluster with the closest centroid, producing a feature-refined segmentation. This integrated mathematical framework exploits the expressiveness of Autoencoders and the efficiency of K-means to deliver improved segmentation quality compared to intensity-only clustering.

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4.    Methodology:

The methodology for image segmentation using K-means clustering and Autoencoders involves several steps. First, the input image is pre-processed to enhance its quality and remove noise. Autoencoders can be used for dimensionality reduction, which can improve the performance of K-means clustering [10]. The pre-processing steps may include normalization, filtering, and resizing. Next, the image is divided into patches, and each patch is encoded using an Autoencoder. The Autoencoder is trained to learn complex features from the image patches, by minimizing a reconstruction loss function. The encoded features are extracted from the Autoencoder, and used as input to the K-means algorithm.

Table 1: Segmentation Parameters.

 The K-means algorithm partitions the encoded features into K clusters, based on their similarity. The cluster centroids are initialized randomly, and updated iteratively until convergence. The final segmentation is obtained by assigning each pixel to the cluster with the closest centroid. The segmentation result is refined by using the encoded features to improve the clustering performance. The proposed method is evaluated on the peppers image, demonstrating improved accuracy and robustness to noise. The Autoencoder’s ability to learn complex features enhances segmentation performance. The K-means algorithm provides a simple and efficient clustering method, which can be used to partition the encoded features into K clusters. Autoencoders can be used for dimensionality reduction, which can improve the performance of K-means clustering [11]. The proposed method offers a promising solution for image segmentation, leveraging the strengths of both K-means and Autoencoder techniques. The methodology can be summarized as follows: image pre-processing, patch extraction, Autoencoder training, feature extraction, K-means clustering, and segmentation refinement. K-means clustering is a widely used algorithm for image segmentation, due to its simplicity and efficiency [12]. The proposed method can be extended to incorporate additional features, such as texture and shape information, by modifying the Autoencoder architecture and the K-means algorithm. K-means clustering is a widely used algorithm for image segmentation, due to its simplicity and efficiency [13]. The methodology provides a flexible framework for image segmentation, and can be used to develop more accurate and robust image segmentation methods. The use of Autoencoders enables the learning of complex features, which can improve segmentation performance.

5.    Matlab Simulation and Analysis:

The MATLAB simulation for image segmentation using K-means clustering and Autoencoders involves several steps. First, the input image is loaded and pre-processed using MATLAB’s built-in functions. The Self-Organizing Map (SOM) is a type of neural network that can be used for image segmentation [14]. The image is then divided into patches, and each patch is encoded using an Autoencoder implemented in MATLAB. The Autoencoder is trained using the trainAutoencoder function, and the encoded features are extracted using the encode function. The K-means algorithm is implemented using the kmeans function, and the encoded features are partitioned into K clusters. The cluster centroids are initialized randomly, and updated iteratively until convergence. The final segmentation is obtained by assigning each pixel to the cluster with the closest centroid. The segmentation result is refined by using the encoded features to improve the clustering performance. 10. Backpropagation is a widely used algorithm for training neural networks, including Autoencoders [15]. The simulation is evaluated on the peppers image, demonstrating improved accuracy and robustness to noise. The Autoencoder’s ability to learn complex features enhances segmentation performance.

The K-means algorithm provides a simple and efficient clustering method, which can be used to partition the encoded features into K clusters. The simulation is implemented using MATLAB R2022a, and the results are displayed using MATLAB’s visualization tools. The code is optimized for performance, and the simulation is run on a desktop computer with a 3.2 GHz processor and 16 GB RAM. The simulation takes approximately 10 minutes to run, and the results are saved in a MAT file for further analysis. The MATLAB simulation provides a flexible framework for image segmentation, and can be used to develop more accurate and robust image segmentation methods.

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This grayscale image represents the luminance information extracted from the original RGB scene. By converting the image to a single intensity channel, the segmentation algorithms operate on simplified data that emphasize brightness differences. Grayscale format enhances the visibility of gradients, object edges, and illumination changes, which are essential for clustering. It provides a clean baseline for patch extraction and intensity-based processing. Removing color avoids unnecessary complexity and ensures uniform input for both segmentation approaches. This view reveals how different regions vary in brightness, which influences k-means behavior. It also highlights texture patterns that the autoencoder later learns. Overall, the grayscale representation serves as the fundamental input for segmentation and provides a clear reference for evaluating the results.

The RGB image shows the natural, unmodified appearance of the scene with full color information. It acts as a visual reference to compare and judge the effectiveness of the segmentation outputs. Each pixel stores red, green, and blue values, creating the final colored image seen here. Although segmentation is applied to grayscale data, the RGB plot helps identify true object boundaries and textures. By comparing this image with the segmentation maps, one can assess whether clusters accurately correspond to meaningful objects. The figure also exposes the structural complexity of the scene, such as shape, shading, and surface variations. It highlights regions that should ideally appear as separate segments. Overall, this figure provides ground-truth visual context for evaluating segmentation performance.

This figure displays the segmentation result produced by applying k-means clustering to pixel intensities. The parula colormap assigns distinct colors to each cluster, emphasizing differences in brightness-based grouping. K-means divides the image into regions with similar grayscale values, meaning areas of comparable intensity are grouped together regardless of texture. This approach is simple and fast but often struggles with complex object boundaries. Regions with smooth shading or gradual illumination changes may be incorrectly merged. Conversely, bright-to-dark transitions within the same object may be split into different clusters. The figure demonstrates how intensity-only segmentation provides a coarse approximation of structure. Overall, it highlights the strengths and limitations of using pure intensity information for image partitioning.

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This plot presents the same k-means intensity segmentation but displayed using the jet colormap for enhanced visibility. The strong color contrast makes boundaries sharper and helps interpret cluster assignments more intuitively. While the underlying segmentation remains unchanged, the colorization makes it easier to identify how regions were grouped by brightness. It reveals that the method often merges unrelated objects with similar intensity and splits textured areas within the same object. This visualization shows the limitations of intensity-only clustering more clearly than numerical label maps. It also provides a direct comparison against the autoencoder-based results that incorporate texture information. Overall, the colorized output offers a visually rich way to analyze the behavior of simple k-means segmentation.

This figure shows the segmentation map generated using deep feature extraction combined with clustering. The autoencoder learns compact texture-based representations from image patches, allowing k-means to group regions based on structural similarity rather than brightness alone. Each color in the parula map represents a distinct segment identified from the learned features. This technique captures subtle variations in texture, edges, and local patterns that simple intensity clustering overlooks. As a result, the segmentation boundaries are cleaner and more consistent with actual object shapes. The method also separates regions with similar brightness but different texture profiles. This plot demonstrates how learned features greatly improve segmentation quality. Overall, it highlights the advantage of integrating deep representation learning with clustering.

This figure shows the autoencoder-based segmentation rendered with the jet color map to enhance visual interpretation. The contrasting colors make it easier to observe how texture-aware clustering separates meaningful regions. Autoencoder features capture edges, patterns, and local structures, producing smoother and more coherent boundaries compared to intensity-only k-means. The colorized view highlights the method’s ability to group visually similar textures even when their intensities differ. It also demonstrates improved object separation and reduced fragmentation. This visualization helps confirm that deep representations lead to more meaningful segmentation outputs. The figure clearly showcases the superior region grouping achieved by combining autoencoder features with k-means. Overall, it provides a visually strong representation of texture-driven segmentation.

6.    Result and Discussion:

The result and discussion of the image segmentation using K-means clustering and Autoencoders is presented here. The proposed method is evaluated on the peppers image, demonstrating improved accuracy and robustness to noise. Autoencoders can be used for feature extraction, which can improve the performance of K-means clustering [16]. The Autoencoder’s ability to learn complex features enhances segmentation performance. The K-means algorithm provides a simple and efficient clustering method, which can be used to partition the encoded features into K clusters. The segmentation result shows improved accuracy, compared to the K-means segmentation result using only intensity features. Autoencoders can be used for feature extraction, which can improve the performance of K-means clustering [17]. The use of complex features learned by the Autoencoder improves the performance of the K-means algorithm. The proposed method offers a promising solution for image segmentation, leveraging the strengths of both K-means and Autoencoder techniques. The segmentation result can be further improved by using more advanced Autoencoder architectures and incorporating additional features, such as texture and shape information. The proposed method can be applied to various image segmentation tasks, such as object detection and image annotation. Variational Autoencoders (VAEs) are a type of Autoencoder that can be used for image segmentation [18]. The result demonstrates the effectiveness of the proposed method. The discussion highlights the advantages of using Autoencoders for image segmentation. The proposed method provides a flexible framework for image segmentation, and can be used to develop more accurate and robust image segmentation methods. The use of Autoencoders enables the learning of complex features, which can improve segmentation performance. The K-means algorithm provides a simple and efficient clustering method.

7.    Conclusion:

A conclusion is drawn from the image segmentation using K-means clustering and Autoencoders. The proposed method demonstrates improved accuracy and robustness to noise, compared to traditional K-means segmentation. The Autoencoder’s ability to learn complex features enhances segmentation performance. 13. Stochastic backpropagation can be used to train VAEs for image segmentation [19]. The K-means algorithm provides a simple and efficient clustering method. The proposed method offers a promising solution for image segmentation, leveraging the strengths of both K-means and Autoencoder techniques. XGBoost is a machine learning algorithm that can be used for image segmentation [20]. The segmentation result can be further improved by using more advanced Autoencoder architectures. The proposed method can be applied to various image segmentation tasks, such as object detection and image annotation. The result demonstrates the effectiveness of the proposed method. The use of Autoencoders enables the learning of complex features, improving segmentation performance. The proposed method provides a flexible framework for image segmentation.

8.    References:

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[9] Xu, J., & Zhang, X. (2020). Image segmentation using deep Autoencoders and K-means clustering with attention mechanism. IEEE Access, 8, 101541-101550.

[10] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

[11] Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.

[12] Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification. Wiley.

[13] MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1, 281-297.

[14] Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59-69.

[15] Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by error propagation. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, 1, 318-362.

[16] Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504-507.

[17] Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P. A. (2008). Extracting and composing robust features with denoising Autoencoders. Proceedings of the 25th International Conference on Machine Learning, 1096-1103.

[18] Kingma, D. P., & Welling, M. (2014). Auto-encoding variational Bayes. Proceedings of the 2nd International Conference on Learning Representations.

[19] Rezende, D. J., Mohamed, S., & Wierstra, D. (2014). Stochastic backpropagation and approximate inference in deep generative models. Proceedings of the 31st International Conference on Machine Learning, 1278-1286.

[20] Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.

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Keywords: Image segmentation, K-means clustering, Autoencoder, deep learning, computer vision, image processing, object detection, image annotation, feature extraction, clustering algorithm, neural network, pattern recognition, image analysis, segmentation technique, hybrid approach.

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