Deep learning models for image segmentation - Deep Learning in Computer Vision
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Introduction
In this article, we will explore the application of deep learning, specifically convolutional neural networks (CNNs), to semantic segmentation tasks in computer vision. Semantic segmentation involves classifying each pixel in an image, creating a detailed map that indicates the areas occupied by different objects. Our goal is to produce an output image where every pixel is assigned a corresponding label.
Understanding Semantic Segmentation
The foundational concept behind using CNNs for semantic segmentation is to reduce the segmentation task to classification. By leveraging the activation maps generated by the hidden layers of a CNN, we can derive useful insights regarding which pixels exhibit the most activation associated with each class.
Converting CNNs for Segmentation
To adapt a traditional CNN (pre-trained for tasks such as classification on datasets like ImageNet) into a fully convolutional network (FCN) suitable for segmentation tasks, several modifications are made. The last fully connected layer, typically found in standard CNNs, is transformed into a convolutional layer with a receptive field size of one. This conversion allows for some degree of localization, helping us understand where activations occur in the input image.
Interestingly, the loss function used during this segmentation process remains consistent with classification tasks—multi-class cross-entropy is employed instead of typical image loss functions. This is because fundamentally, pixel classification is still regarded as a classification problem, even though the outcome is an image.
Limitations of the Standard Approach
Though this standard approach has its advantages, it suffers from a loss of spatial resolution due to repeated downscaling through various layers, diminishing the clarity of the output images.
Encoder-Decoder Architectures
An alternative method for semantic segmentation leverages downsampling and upsampling strategies, commonly known as encoder-decoder architectures. This method is particularly efficient in maintaining the input image dimensions through both the encoding and decoding stages.
Each image, originally size n × n, is first compressed by a series of convolutions, followed by a corresponding expansion to yield an output that matches the original dimensions. This is typically accomplished through skip connections—links that preserve critical information from earlier layers—and the use of unpooling and transposed convolution operations during the decoding stage.
A well-known model that embodies this architecture is the SegNet model, which utilizes a VGG-structured encoder. While SegNet is effective for tasks such as road scene classification on datasets like CamVid, it has shown varied performance in medical image segmentation.
Transposed Convolutions and Unpooling
Within the SegNet architecture, transposed convolution is a key mechanism. It effectively reverses the downscaled effects of earlier convolutional layers during forward propagation. The core idea involves applying the appropriate kernel and stride sizes while using zero padding to achieve the desired output size.
Unpooling, on the other hand, serves as an approximation for the non-invertible max pooling operation. Techniques such as interpolation, utilizing bilinear methods, can reconstruct the data, while recording max locations during pooling can help recover original values more accurately.
Final Architecture Overview
A unit for down-sampling typically consists of repeated applications of 3x3 convolutions followed by a rectification layer and 2x2 max pooling operations. This down-sampling phase effectively doubles the number of feature channels at each step.
During the upsampling phase, we employ transposed convolutions complemented by additional 2x2 convolutions and concatenation with corresponding feature maps from the down-sampling phase. This step is crucial, as it ensures high-quality reconstructions despite the loss of border pixels resulting from convolutions. The network culminates in a one-by-one convolution to map final features to the desired class labels.
The unit architecture generally comprises 23 convolutional layers and has proven effective, particularly in medical image segmentation tasks.
Conclusion
In summary, semantic segmentation can be approached as a pixel-wise classification task. While direct application of pre-trained CNNs is possible, encoder-decoder architectures tend to deliver superior performance in segmentation challenges. These networks utilize specialized layers for upsampling and depend on skip connections to facilitate gradient propagation throughout the network.
Keywords
- Deep Learning
- Convolutional Neural Networks (CNN)
- Semantic Segmentation
- Pixel Classification
- Fully Convolutional Networks (FCN)
- Encoder-Decoder Architecture
- Downsampling
- Upsampling
- Transposed Convolution
- Unpooling
FAQ
Q1: What is semantic segmentation?
A1: Semantic segmentation is the task of classifying each pixel in an image to create a map of detected object areas.
Q2: How do convolutional neural networks (CNNs) work for semantic segmentation?
A2: CNNs can be adapted for semantic segmentation by converting the last fully connected layer into a convolutional layer, allowing pixel-wise classification.
Q3: What is the encoder-decoder architecture?
A3: The encoder-decoder architecture is a structure in neural networks that compresses an image with convolutions (encoding) and then reconstructs it to the original size (decoding) using upsampling techniques.
Q4: What is transposed convolution?
A4: Transposed convolution is a type of operation used in the upsampling part of neural networks that effectively reverses the downscaled effects of regular convolutions.
Q5: Why is downsampling necessary in neural networks for segmentation?
A5: Downsampling reduces the spatial dimensions of the image while increasing the number of feature channels, allowing the network to learn more abstract representations of the input data.
