What actions would allow the Specialist to get relevant numerical representations?

A Machine Learning Specialist is developing recommendation engine for a photography blog Given a picture, the recommendation engine should show a picture that captures similar objects The Specialist would like to create a numerical representation feature to perform nearest-neighbor searches

What actions would allow the Specialist to get relevant numerical representations?
A . Reduce image resolution and use reduced resolution pixel values as features
B . Use Amazon Mechanical Turk to label image content and create a one-hot representation indicating the presence of specific labels
C . Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer
D . Average colors by channel to obtain three-dimensional representations of images.

View Answer

Answer: C

Explanation:

A neural network pre-trained on ImageNet is a deep learning model that has been trained on a large dataset of images containing 1000 classes of objects. The model can learn to extract high-level features from the images that capture the semantic and visual information of the objects. The penultimate layer of the model is the layer before the final output layer, and it contains a feature vector that represents the input image in a lower-dimensional space. By running images through a pre-trained neural network and collecting the feature vectors from the penultimate layer, the Specialist can obtain relevant numerical representations that can be used for nearest-neighbor searches. The feature vectors can capture the similarity between images based on the presence and appearance of similar objects, and they can be compared using distance metrics such as Euclidean distance or cosine similarity. This approach can enable the recommendation engine to show a picture that captures similar objects to a given picture.

Reference: ImageNet – Wikipedia

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