Image Classification Using Bag of Visual Words (BoVW)

Abstract

In this paper two main stages for image classification has been presented. Training stage consists of collecting images of interest, and apply BOVW on these images (features extraction and description using SIFT, and vocabulary generation), while testing stage classifies a new unlabeled image using nearest neighbor classification method for features descriptor. Supervised bag of visual words gives good result that are present clearly in the experimental part where unlabeled images are classified although small number of images are used in the training process.

Audiovisual, Genre, Neural and Topical Textual Embeddings for TV Programme Content Representation

“…SIFT provides 128D feature vector against each keypoint in an image. Once the features are extracted, BoVW model [24] was used to quantise all image descriptors and create a visual word vocabulary for each programme using K-means with k =300.…”

A Clinical Decision Support Tool to Detect Invasive Ductal Carcinoma in Histopathological Images Using Support Vector Machines, Naïve-Bayes, and K-Nearest Neighbor Classifiers

“…Since an image can have multiple features, for each image, features are extracted then a visual dictionary or bag of visual words is generated using k-means clustering [21]. This visual dictionary, represented by a collection of histograms, will be used in training machine learning algorithms in order to classify a new input image [22][23][24]. In this study, we extracted the features of each image in the training set using ORB (Oriented FAST and Rotated BRIEF), applied normalization on each feature and then reduced the number of features via KMeans clustering.…”

“…K-Means clustering is an algorithm used to find groups in data where k represents the number of groups or clusters. It is typically used as an unsupervised learning algorithm but is also commonly used as a vector quantization step in codebook generation in the BoVW Model [24]. Each cluster contains a centroid, a data point at the center of a cluster representing a multi-dimensional average of the cluster [32].…”

The Combination of Adaptive Convolutional Neural Network and Bag of Visual Words in Automatic Diagnosis of Third Molar Complications on Dental X-Ray Images

“…In this research, we use feature extraction methods namely Scale-Invariant Feature Transform (SIFT) [20] and Speeded Up Robust Features (SURF) [21][22][23] as they give more key-points, which are needed in this case. Since these feature extraction methods do not use the bounding box concept [5,24], we can achieve a bounding box concept by ignoring certain regions of the image using "mask" and specifying X and Y positions on the images.…”

“…All extracted features are kept in a visual BoVW [23] as unique patterns which can be found in an image. Features are kept in different groups [21,23]. All previous steps are done before feeding the feature into a Neural Network or a SVM for training and afterward prediction.…”