Image segmentation using deep learning

Introduction

Image segmentation is a key task in computer vision where the aim is to separate an image into different regions or objects. One of the most popular approaches to image segmentation is using deep learning, where neural networks are trained to categorize pixels in an image into different classes. In this blog post, we will discuss the basics of image segmentation using deep learning and explore a popular neural network architecture for this task.

Semantic Segmentation

Semantic segmentation is a type of image segmentation where the goal is to classify each pixel in an image into a specific category. For example, if we are given an image of a street scene, we would want to segment each pixel into different classes such as road, sidewalk, cars, traffic lights, etc.

One of the most popular neural network architectures for semantic segmentation is the Fully-Convolutional Network (FCN). This model takes an image as input and outputs a segmentation mask where each pixel is classified into a specific category. Here is an example of an FCN architecture:

input = Input(shape=input_shape)

# Encoding Phase
conv1 = Conv2D(64, (3, 3), activation='relu', padding='same')(input)
conv1 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

conv2 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool1)
conv2 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

conv3 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool2)
conv3 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv3)
conv3 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)

# Decoding Phase
conv4 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool3)
conv4 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv4)
conv4 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv4)
drop4 = Dropout(0.5)(conv4)

up5 = UpSampling2D(size=(2, 2))(drop4)
conv5 = Conv2D(256, (3, 3), activation='relu', padding='same')(up5)
conv5 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv5)
conv5 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv5)

up6 = UpSampling2D(size=(2, 2))(conv5)
conv6 = Conv2D(128, (3, 3), activation='relu', padding='same')(up6)
conv6 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv6)

up7 = UpSampling2D(size=(2, 2))(conv6)
conv7 = Conv2D(64, (3, 3), activation='relu', padding='same')(up7)
conv7 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv7)

out = Conv2D(num_classes, (1, 1), activation='softmax')(conv7)

model = Model(input, out)

In the above code, we first define the input shape of our image. We then define the encoding phase of the network, which consists of several convolutional and pooling layers. This phase is responsible for extracting features from the image. In the decoding phase, we use up-sampling layers to increase the resolution of the image, followed by several convolutional layers to refine the segmentation mask. The final layer uses softmax activation to output a probability distribution over the different classes.

Training the FCN

To train the FCN, we first need a dataset consisting of images and their corresponding segmentation masks. One popular dataset for semantic segmentation is the PASCAL VOC dataset, which consists of images and their corresponding object annotations.

We can use a method called data augmentation to increase the size of our dataset. Data augmentation involves applying random transformations such as rotations, flips, and zooms to our images, which creates new variations of the original dataset. This helps to prevent overfitting and improves the generalization performance of our model.

To train our model, we use a loss function called categorical cross-entropy, which measures the difference between the predicted segmentation mask and the ground truth mask. We also use an optimizer called Adam, which adjusts the weights of our neural network to minimize the loss.

Here is an example code snippet for training the FCN:

model.compile(optimizer='adam', loss='categorical_crossentropy')

model.fit(x=train_images, y=train_masks, batch_size=batch_size, epochs=num_epochs)

Evaluation

To evaluate the performance of our model, we can use metrics such as accuracy and intersection over union (IOU). Accuracy measures the percentage of correctly classified pixels, while IOU measures the overlap between the predicted segmentation mask and the ground truth mask.

Here is an example code snippet for evaluating the FCN:

test_loss, test_acc = model.evaluate(x=test_images, y=test_masks)
print('Test accuracy:', test_acc)

predictions = model.predict(x=test_images)
iou_score = calculate_iou(predictions, test_masks)
print('IOU score:', iou_score)

Conclusion

In this blog post, we covered the basics of image segmentation using deep learning, specifically semantic segmentation using the FCN architecture. We discussed the importance of data augmentation, loss functions, and optimizers in training our neural network. Finally, we showed how to evaluate the performance of our model using metrics such as accuracy and IOU. Deep learning has revolutionized the field of computer vision, and image segmentation is just one example of its many applications.

Additional Resources