Bag of visual words for object recognition

With the continuous development of the field of computer vision, people's research on object recognition is becoming more and more in-depth. Among them, Bag of Visual Words (BoW) is a commonly used object recognition method. This article will introduce the principles, advantages and disadvantages of the bag of visual words method, and give examples. The bag of visual words method is an object recognition method based on local features of the image. It divides the image into multiple small regions and extracts feature descriptors for each region. Then, these feature descriptors are grouped into a visual bag of words through a clustering algorithm, where each bag of words represents a specific local feature. In the object recognition stage, the feature descriptor of the input image is combined with the visual word

1. Principle

Bag of visual words is a classic Image classification methods. It works by extracting local features in an image and using a clustering algorithm to cluster these features into a set of visual words. Then, by counting the frequency of each visual word appearing in the image, the image is represented as a fixed-length vector, that is, the bag of visual words representation. Finally, the bag of visual words is fed into the classifier for classification. This method is widely used in image recognition tasks because it is able to capture the important features in the image and represent them into a vector form that can be used by the classifier.

2. Advantages and Disadvantages

Advantages:

(1) The bag of visual words method is simple , easy to implement;

(2) It can extract the local features of the image and has certain robustness to the rotation, scaling and other transformations of the object;

(3) For smaller data sets, it has better classification effect.

Disadvantages:

(1) The bag of visual words method does not take into account the spatial relationship between features. For the posture changes and parts of the object, In situations such as occlusion, the classification effect is poor;

(2) The number of clusters needs to be manually set. For different data sets, the number of clusters needs to be reset, resulting in poor versatility. ;

(3) It cannot take advantage of the excellent feature representation in deep learning, so the classification effect is limited.

3. Examples

The following takes the MNIST data set as an example to illustrate the application of bag of visual words.

The MNIST data set is a handwritten digit classification data set, containing 60,000 training set samples and 10,000 test set samples. Each sample is a 28x28 grayscale image representing a handwritten digit. The code is implemented as follows:

import numpy as np
import cv2
from sklearn.cluster import KMeans
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# 读取MNIST数据集
train_images = np.load('mnist_train_images.npy')
train_labels = np.load('mnist_train_labels.npy')
test_images = np.load('mnist_test_images.npy')
test_labels = np.load('mnist_test_labels.npy')

# 特征提取
features = []
sift = cv2.xfeatures2d.SIFT_create()
for image in train_images:
    keypoints, descriptors = sift.detectAndCompute(image, None)
    features.append(descriptors)
features = np.concatenate(features, axis=0)

# 聚类
n_clusters = 100
kmeans = KMeans(n_clusters=n_clusters)
kmeans.fit(features)

# 计算视觉词袋
train_bow = []
for image in train_images:
    keypoints, descriptors = sift.detectAndCompute(image, None)
    hist = np.zeros(n_clusters)
    labels = kmeans.predict(descriptors)
    for label in labels:
        hist[label] += 1
    train_bow.append(hist)
train_bow = np.array(train_bow)

test_bow = []
for image in test_images:
    keypoints, descriptors = sift.detectAndCompute(image, None)
    hist = np.zeros(n_clusters)
    labels = kmeans.predict(descriptors)
    for label in labels:
        hist[label] += 1
    test_bow.append(hist)
test_bow = np.array(test_bow)

# 分类
knn = KNeighborsClassifier()
knn.fit(train_bow, train_labels)
pred_labels = knn.predict(test_bow)

# 计算准确率
acc = accuracy_score(test_labels, pred_labels)
print('Accuracy:', acc)

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