Understanding Image Content
Anomaly Detection
Image Segmentation
Figure 4.16: Autonomous vehicle vision with image segmentation
Figure 4.17: Clusters of image recognition analysis
Loading and Preprocessing Images
Unstructured data is diverse and can require a lot of time and computational resources.
The next step is to convert the 'resized_images' and 'labels' lists to numpy arrays.
Clustering without Feature Engineering
The data can now be visualized using the familiar TSNEVisualizer tool from the yellowbrick library.
Figure 4.19: Dendrogram categorizing data into two clusters
Finally, the homogeneity, completeness, and adjusted Rand metrics
Clustering with Feature Selection
Figure 4.20: Clusters visualization
The dendrogram suggests 5 clusters,
Clustering Using Neural Networks
The TensorFlow and Keras libraries that were introduced in the previous lesson can be used to access
The results are impressive. The new visualization reveals clearly separated,
The following code uses Agglomerative Clustering and reports the metric scores for both 4 and 10 clusters:
Mention an advantage that unsupervised vision techniques have over supervised techniques.
You are given a numpy array X_flat that includes flattened images. Each row in the array
List some advantages of using Deep Learning over other traditional image clustering methods?
You are given a numpy array X_flat that includes flattened images. Each row in the array
Describe how clustering with neural networks is applied in image analysis.
