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Object-based image classification involves segmenting an image into meaningful objects and classifying them based on their features. This approach is widely used in computer vision tasks such as land cover mapping, object detection, and medical imaging. Below is a step-by-step guide to implementing object-based image classification in Python.
1. Image Segmentation
Segmentation divides an image into regions or objects based on characteristics like color, texture, or intensity.
import cv2import numpy as np# Load the imageimage = cv2.imread("image.jpg")gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)# Apply thresholding for segmentation_, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)# Find contours (segmented regions)contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)Copied!✕Copy2. Feature Extraction
Extract features like shape descriptors, texture, or color histograms from each segmented object.
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