Example of video data labeling using k-means clustering with a color histogram – Exploring Video Data
Let us see example code for performing k-means clustering on video data using the open source scikit-learn Python package and the Kinetics human action dataset....
Read out all
Frame visualization – Exploring Video Data
We create a line plot to visualize the frame intensities over the frame indices. This helps us understand the variations in intensity across frames:# Frame...
Read out all
Example of video data labeling using k-means clustering with a color histogram – Exploring Video Data
Let us see example code for performing k-means clustering on video data using the open source scikit-learn Python package and the Kinetics human action dataset....
Read out all
Frame visualization – Exploring Video Data
We create a line plot to visualize the frame intensities over the frame indices. This helps us understand the variations in intensity across frames:# Frame...
Read out all
Appearance and shape descriptors – Exploring Video Data
Extract features based on object appearance and shape characteristics. Examples include Hu Moments, Zernike Moments, and Haralick texture features. Appearance and shape descriptors are methods...
Read out all
Optical flow features – Exploring Video Data
We will extract features based on the optical flow between consecutive frames. Optical flow captures the movement of objects in video. Libraries such as OpenCV...
Read out all
Extracting features from video frames – Exploring Video Data
Another useful technique for the EDA of video data is to extract features from each frame and analyze them. Features are measurements or descriptors that...
Read out all
In
Loading video data using cv2 – Exploring Video Data
Exploratory Data Analysis (EDA) is an important step in any data analysis process. It helps you understand your data, identify patterns and relationships, and prepare...
Read out all
In
Technical requirements – Exploring Video Data
In today’s data-driven world, videos have become a significant source of information and insights. Analyzing video data can provide valuable knowledge about human actions, scene...
Read out all
In
CNN using image data augmentation with the CIFAR-10 dataset – Labeling Image Data Using Data Augmentation-2
The following line adds a max-pooling layer with a pool size of (2, 2), which reduces the spatial dimensions of the input by taking the...
Read out all
In
Generating labels for customer reviews (sentiment analysis) – Labeling Text Data-2
The maximum sequence length is determined by finding the length of the longest sequence. The pad_sequences function is used to pad the sequences to the...
Read out all
In
Generating labels for customer reviews (sentiment analysis) – Labeling Text Data-1
Customer reviews are a goldmine of information for businesses. Analyzing sentiment in customer reviews helps in understanding customer satisfaction, identifying areas for improvement, and making...
Read out all
In
Practical example of a CNN using data augmentation – Labeling Image Data Using Data Augmentation-1
Let us see how to implement image data augmentation on a CNN. To do so, you can follow these steps: Step 1:...
Read out all