i know what semantic segmentation is and i know how to do semantic segmentation using deep learning but my question here can i do semantic segmentation with a traditional way like kmeans or mean shift ckustering? here's what i tried to do import numpy as np import cv2 from sklearn.cluster import MeanShift, estimate_bandwidth #from skimage.color import rgb2lab #Loading original image originImg = cv2.imread('test/2019_00254.jpg') # Shape of original image originShape = originImg.shape # Converting image into array of dimension [nb of …

Mean Shift clustering is an Unsupervised learning that assigns the data points to the clusters iteratively by shifting points towards the mode (mode is the highest density of data points in the region, in the context of the Meanshift). Q1. When mean shift clustering is preferred over K means and other methods like DBscan? Q2. For what type of data mean clustering gives best result? Q3. Can Mean Shift clustering be used for text classification/clustering problems?

I am trying to compare different clustering algorithms for my text data. I first calculated the tf-idf matrix and used it for the cosine distance matrix (cosine similarity). Then I used this distance matrix for K-means and Hierarchical clustering (ward and dendrogram). I want to use the distance matrix for mean-shift, DBSCAN, and optics. Below is the part of the code showing the distance matrix. from sklearn.feature_extraction.text import TfidfVectorizer #define vectorizer parameters tfidf_vectorizer = TfidfVectorizer(max_df=0.8, max_features=200000, min_df=0.2, stop_words='english', use_idf=True, tokenizer=tokenize_and_stem, …

I am trying to understand a textbook exercise I am doing. I have an array of data force_b = array([0.172, 0.142, 0.037, 0.453, 0.355, 0.022, 0.502, 0.273, 0.72 ,0.582, 0.198, 0.198, 0.597, 0.516, 0.815, 0.402, 0.605, 0.711, 0.614, 0.468]) with the mean = 0.4191000000000001 I have another mean of 0.55 and I have to shift the data of the array above so that I get an array with the mean of 0.55 The solution in the exercise is translated_force_b = …

I am using sklearn.clustering to work with some text data and the MeanShift algorithm. I have: Done all standard NLP data prep like lemmatizing, removing stop words, etc. Used the TfidfVectorizer to create my word vectors on 80k-plus records The vectorizer gives me a sparse array so I converted it using a standard .toarray() command I made a call to sklearn Meanshift and then accepted all of the default parameters. The call looks like meanshift = MeanShift().fit(fitted_vector_data.toarray()) and results in …