Learn how image classification, a key computer vision technique, is used to identify and label objects like fruits in an image with high accuracy percentages.
Table of Contents
Question
You are processing an image using Computer Vision API, which generated an annotated image shown in the exhibit.
- orange 96.77%
- banana: 97.90%
- apple 98.21%
Which type of computer vision was used?
A. object detection.
B. image classification.
C. optical character recognition (OCR).
D. semantic segmentation.
Answer
B. image classification.
Explanation
Image classification is the type of computer vision used to process the image and generate the annotated results shown in the exhibit. Image classification is a supervised learning technique that involves training a model on a dataset of labeled images. The model learns to recognize patterns and features associated with each label or class.
Once trained, the image classification model can be applied to new, unseen images to predict the class or label for each object detected. In this case, the model has classified the objects as an orange, banana, and apple with high confidence scores of 96.77%, 97.90%, and 98.21%, respectively.
The other options are different computer vision techniques used for other purposes:
A. Object detection – identifying and locating objects within an image, often with bounding boxes
C. Optical character recognition (OCR) – extracting text from images
D. Semantic segmentation – pixel-wise classification of an image into distinct classes or categories
Therefore, the correct answer is B. Image classification is the specific computer vision technique used to classify the fruits in the given image.
In this scenario, the Computer Vision API is generating labels (orange, banana, apple) with confidence scores, which indicates that it is classifying the types of objects present in the image. This aligns with image classification, where the task is to identify and assign categories to images based on their content.
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