Discover how to calculate the feature vector dimension for a 120×80 pixel color image in CNNs. Learn the role of RGB channels and dimensions in this detailed explanation. To determine the dimension of the feature vector for a color image of size 120 x 80 pixels, we need to consider the structure of color images and their representation in Convolutional Neural Networks (CNNs).
Question
Given a color image of size 120 x 80 pixels, what is the dimension of its feature vector?
A. 9,600
B. 28,800
C. 8,400
D. 25,200
Answer
B. 28,800
Explanation
Understand Image Representation in CNNs
A color image is typically represented as a three-dimensional tensor:
- Height (H): 120 pixels
- Width (W): 80 pixels
- Depth (D): 3 channels (Red, Green, Blue)
Thus, the total number of elements (or features) in the image is calculated as:
Total Features=H×W×D
Calculation
For a 120 x 80 pixel RGB image:
Total Features=120×80×3=28,800
This value represents the dimension of the feature vector if the image is flattened into a one-dimensional array before being processed by fully connected layers in a CNN.
- Each pixel in an RGB image has three values corresponding to the intensity levels of Red, Green, and Blue channels.
- Flattening the image into a feature vector combines all pixel values across all channels into one long array.
- The computation above confirms that for an RGB image with dimensions 120×80, the feature vector dimension is 28,800.
This calculation assumes no further processing (e.g., pooling or convolutional operations) that might reduce the spatial dimensions before flattening.
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