region based segmentaton methods Flashcards
(52 cards)
What do region-based approaches seek to differentiate using image properties?
Regions based on image properties such as:
* Color
* Texture
* Shape
Region-based approaches utilize descriptors to segment images according to pixel similarity.
What are the two main types of classification methods in region approaches?
Classification methods:
* Hard classification
* Fuzzy classification
These methods define how pixels are grouped into classes.
Name two supervised classification methods mentioned in the region approach.
Supervised classification methods:
* Support Vector Machine (SVM)
* Neural networks (FFNN)
These methods require labeled training data to learn patterns.
What is the purpose of the K-Means algorithm in unsupervised classification?
K-Means algorithm is used to group data into clusters without predefined labels based on pixel similarity.
It is an iterative method that assigns pixels to clusters based on distance to cluster centroids.
What are the three methods of segmentation by region mentioned?
Segmentation methods by region:
* Division
* Fusion
* Split-Merge
These methods involve different strategies for creating regions from the image.
What does the division technique in segmentation entail?
The division technique involves partitioning the image into homogeneous regions according to a given criterion.
It continues until homogeneous classes are obtained.
What is a major drawback of the quad-tree region division method?
The major drawback is over-segmentation.
This can lead to too many regions being created that do not represent meaningful segments.
What is the principle behind region fusion in segmentation?
Region fusion tests a predicate for each pixel neighborhood and merges regions based on shared characteristics.
This process continues until a stopping criterion is satisfied.
What is meant by ‘Mean-shift segmentation’?
Mean-shift segmentation iteratively searches for local maxima in the gray level density distribution.
It involves moving initialization points based on local averages until convergence.
What are the steps involved in supervised classification?
Steps in supervised classification:
1. Dataset Preparation
2. Feature Extraction
3. Training the Classification Algorithm
4. Validation Phase
5. Performance Evaluation
6. Final Testing & Model Deployment
Each step is crucial for creating an effective classification model.
What is the difference between supervised and unsupervised classification?
Supervised classification requires labeled data, while unsupervised classification groups data without predefined labels.
Supervised methods learn from examples, whereas unsupervised methods identify patterns independently.
What is the function of the validation set in a supervised classification process?
The validation set is used to fine-tune hyperparameters and prevent overfitting.
It helps ensure that the model generalizes well to unseen data.
Fill in the blank: The ______ method assigns a sample based on the majority class of its k nearest neighbors.
k-Nearest Neighbors (KNN)
KNN is a popular algorithm used for classification tasks.
True or False: The dataset split for supervised classification can be done using a Train-Validate-Test split.
True
This method allows for independent evaluation of model performance.
What is the role of the test set in a supervised classification model?
The test set is used for the final evaluation of model performance.
It is completely independent and helps assess the model’s ability to generalize.
What is the purpose of using cross-validation in model training?
Cross-validation optimizes model generalization by rotating validation across subsets.
It ensures the model is tested on multiple data segments.
List four traditional supervised classifiers mentioned.
Traditional supervised classifiers:
* Minimum Distance (MD)
* k-Nearest Neighbors (KNN)
* Parallelepiped Method
* Maximum Likelihood (MV)
These classifiers use different strategies to assign data points to classes.
What is the significance of feature extraction in classification?
Feature extraction involves extracting relevant attributes from training samples for classification.
These attributes are essential for the classifier to learn patterns.
What does Convolutional Neural Networks (CNNs) specialize in?
Image classification using deep learning techniques
What is the Minimum Distance (MD) classification method based on?
Euclidean distance
How is a pixel classified in the Minimum Distance method?
Into the class to which it is closest
What are class centers estimated from in the Minimum Distance classification?
Training pixels (barycenter, mean)
What does the k-Nearest Neighbors (KNN) method rely on?
The notion of similarity between learning entities and entities to be classified
