[AI from Scratch] Episode 288: Introduction to Segmentation — Classifying Images at the Pixel Level

Recap and Today’s Theme

Hello! In the previous episode, we discussed the workings and implementation of SSD (Single Shot MultiBox Detector), a real-time object detection model. SSD processes images in a single pass, detecting objects at multiple scales, offering a balance of speed and accuracy.

Today, we will focus on segmentation. Segmentation is a technique used to classify each pixel in an image into different classes, offering a more detailed analysis than object detection. This article introduces the basic concepts of segmentation and some of its most representative methods.

What is Segmentation?

Segmentation involves assigning a label to each pixel in an image, classifying it on a pixel-by-pixel basis. This enables the identification of different regions within the image (e.g., roads, buildings, people), allowing for a fine-grained understanding of boundaries and shapes. Segmentation can be divided into two main categories:

1. Semantic Segmentation:

• In this approach, pixels are classified into classes, treating all pixels belonging to the same class as the same entity. For example, all pixels belonging to “people” or “buildings” are grouped, without distinguishing between individual instances (e.g., different people).

1. Instance Segmentation:

• This method goes beyond semantic segmentation, identifying and separating individual objects (instances). For example, if there are three people in an image, each person is treated as a separate entity and labeled accordingly.

Applications of Segmentation

Segmentation is widely used in various fields where detailed information extraction from images is crucial:

• Autonomous Driving: Segmentation allows onboard cameras to recognize roads, pedestrians, vehicles, traffic signals, and more, providing support for driving assistance.
• Medical Image Analysis: Segmentation helps accurately identify organs or lesions from MRI or CT scans, aiding in diagnosis.
• Agriculture: Drones capture images of farmland, and segmentation is used to distinguish crops from weeds, supporting efficient farming operations.

Basic Segmentation Methods

There are several segmentation techniques, but here we will focus on the most common deep learning-based approaches.

1. Semantic Segmentation

In semantic segmentation, the entire image is classified at the pixel level into classes. For example, pixels are labeled as background, road, car, or pedestrian. This approach classifies groups of pixels but doesn’t differentiate between individual objects of the same class.

Representative Models

• FCN (Fully Convolutional Network):
• FCN is an extension of CNN that performs pixel-level classification by replacing fully connected layers with convolutional layers, preserving spatial resolution while predicting each pixel’s class.
• U-Net:
• Widely used in medical image analysis, U-Net has an encoder-decoder structure. The encoder extracts features from the image, and the decoder restores the original resolution while predicting the class of each pixel.

2. Instance Segmentation

Instance segmentation adds to semantic segmentation by distinguishing between different objects (instances) of the same class. For example, if there are three cars in an image, each car is treated as a separate instance.

Representative Models

• Mask R-CNN:
• An extension of the Faster R-CNN object detection model, Mask R-CNN adds pixel-level masks to detect and classify each object instance. It first detects bounding boxes for objects and then classifies pixels inside the box for segmentation.

Implementing Semantic Segmentation with Python and Keras

Below is an example of implementing a simple semantic segmentation task using Python and Keras. We will use a pre-trained U-Net model to perform segmentation on an image dataset.

1. Installing Required Libraries

pip install tensorflow opencv-python

2. U-Net Implementation Code for Segmentation

The following code demonstrates how to build a U-Net model and classify each pixel in the input image. Here, we use a simple dataset (e.g., the CamVid dataset) for segmentation tasks.

import tensorflow as tf
from tensorflow.keras import layers, models
import numpy as np
import cv2
import matplotlib.pyplot as plt

# U-Net model construction
def build_unet(input_shape):
    inputs = tf.keras.Input(shape=input_shape)

    # Encoder (feature extraction)
    c1 = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(inputs)
    c1 = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(c1)
    p1 = layers.MaxPooling2D((2, 2))(c1)

    c2 = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(p1)
    c2 = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(c2)
    p2 = layers.MaxPooling2D((2, 2))(c2)

    c3 = layers.Conv2D(256, (3, 3), activation='relu', padding='same')(p2)
    c3 = layers.Conv2D(256, (3, 3), activation='relu', padding='same')(c3)
    p3 = layers.MaxPooling2D((2, 2))(c3)

    # Decoder (reconstruction)
    u3 = layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c3)
    u3 = layers.concatenate([u3, c2])
    c4 = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(u3)
    c4 = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(c4)

    u4 = layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c4)
    u4 = layers.concatenate([u4, c1])
    c5 = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(u4)
    c5 = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(c5)

    outputs = layers.Conv2D(1, (1, 1), activation='sigmoid')(c5)

    return models.Model(inputs, outputs)

# Compile the model
input_shape = (128, 128, 3)
unet_model = build_unet(input_shape)
unet_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Display the model summary
unet_model.summary()

3. Key Points in the Code

• U-Net Model Construction: The U-Net model has an encoder and a decoder. The encoder extracts features while the decoder restores the image’s original resolution and predicts pixel classifications.
• Decoder: The decoder upscales the features compressed by the encoder to match the original image size and predict each pixel’s class.
• Conv2DTranspose: This layer performs the opposite of a convolution, expanding the feature map back to its original resolution.

Challenges and Future Prospects in Segmentation

Challenges

• Processing High-Resolution Images:
• High-resolution images require significant computational resources and memory, making it challenging to perform real-time segmentation on edge devices.
• Instance Segmentation Accuracy:
• Unlike semantic segmentation, accurately separating individual object instances requires more sophisticated models, such as Mask R-CNN.

Future Prospects

• Development of Lightweight Models:
• Research is progressing toward creating lightweight, fast segmentation models that can be deployed on mobile and edge devices.
• Automated Data Augmentation:
• Large datasets are essential for segmentation, and techniques for automated data augmentation are being developed to expand datasets efficiently.

Summary

In this episode, we introduced the basics of segmentation, focusing on classifying images at the pixel level using methods like U-Net. Segmentation plays a vital role in extracting detailed information in applications like medical imaging and autonomous driving. In the next episode, we will delve deeper into implementing U-Net, focusing on building segmentation models for medical images.

Next Episode Preview

In the next episode, we will explain how to implement U-Net for medical image segmentation, covering the importance of segmentation in medical image analysis and key implementation details.

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Notes

• Semantic Segmentation: A technique that classifies each pixel in an image into a class without distinguishing between individual instances.
• Instance Segmentation: A technique that separates individual objects (instances) in an image, classifying each pixel accordingly.