Bag of Words Model: Unlocking Visual Intelligence with Bag of Words

By Fouad Sabry

What is Bag of Words Model

In computer vision, the bag-of-words model sometimes called bag-of-visual-words model can be applied to image classification or retrieval, by treating image features as words. In document classification, a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary. In computer vision, a bag of visual words is a vector of occurrence counts of a vocabulary of local image features.

How you will benefit

(I) Insights, and validations about the following topics:

Chapter 1: Bag-of-words model in computer vision

Chapter 2: Image segmentation

Chapter 3: Scale-invariant feature transform

Chapter 4: Scale space

Chapter 5: Automatic image annotation

Chapter 6: Structure from motion

Chapter 7: Sub-pixel resolution

Chapter 8: Mean shift

Chapter 9: Articulated body pose estimation

Chapter 10: Part-based models

(II) Answering the public top questions about bag of words model.

(III) Real world examples for the usage of bag of words model in many fields.

Who this book is for

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Bag of Words Model.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: May 13, 2024

Book preview

Chapter 1: Bag-of-words model in computer vision

The bag-of-words model (BoW model), also known as the bag-of-visual-words model, is a technique used in computer vision for classifying and retrieving images by interpreting their features as words. A bag of words is a sparse vector of word occurrence counts, or a sparse histogram over the vocabulary, used for document classification. In computer vision, a bag of visual words is a vocabulary of local image features that is represented as a vector of occurrence counts.

Using the BoW model, an image can be represented in the same way as a document. Images that contain words also require clarification. Three common procedures—feature detection, feature description, and codebook generation—are used to accomplish this. The histogram representation based on independent features is one way to characterize the BoW model.

Each image is then abstracted by a number of neighborhood patches following feature detection. How the patches should be represented as numerical vectors is the focus of feature representation techniques. Feature descriptors are the names for these numerical vectors. A good descriptor should be flexible enough to account for variations in brightness, rotation, scale, and affine transformations. Scale-invariant feature transform is one of the most well-known identifiers (SIFT). Each patch is transformed by SIFT into a 128-dimensional vector. At this point, the order of the individual vectors in an image is irrelevant, as they are all of the same size (128 for SIFT).

Finally, the BoW model produces a codebook by translating vector-represented patches into codewords (like words in text documents) (analogy to a word dictionary). A codeword can stand in for a group of patches that are all essentially the same. K-means clustering can be performed on all the vectors for a quick and easy solution. The hubs of these newly-learned groups become codewords. The codebook's capacity is equal to the total number of clusters (analogous to the size of the word dictionary).

As a result of the clustering procedure, each image patch is associated with a unique codeword, and the image itself can be represented by a histogram of the codewords.

Several learning methods have been developed by the computer vision research community to take advantage of the BoW model for image-related tasks like object categorization. Unsupervised and supervised models provide a rough categorization of these techniques. When assessing solutions to a problem involving multiple labels, the confusion matrix is a useful tool.

Please see the accompanying notes for this segment.

Suppose the size of codebook is V .

w : each patch w is a V-dimensional vector that has a single component equal to one and all other components equal to zero (For k-means clustering setting, the single component equal one indicates the cluster that w belongs to).

The v th codeword in the codebook can be represented as w^{v}=1 and w^{u}=0 for u\neq v .

\mathbf {w} : each image is represented by \mathbf {w} =[w_{1},w_{2},\cdots ,w_{N}] , all the dots that make up a picture

d_{j} : the j th image in an image collection

c : category of the image

z : theme or topic of the patch

\pi : mixture proportion

Because its NLP counterpart, the BoW model, is an analogy, Computer vision can benefit from generative models originally created for the textual domain.

Simple Naïve Bayes model and hierarchical Bayesian models are discussed.

The simplest one is Naïve Bayes classifier.

Making use of graphical model notation, the Naïve Bayes classifier is described by the equation below.

Each classification is assumed to have its own unique distribution across the various codebooks in this model, and that there is a clear distinction between the distributions of the various groups.

Consider the categories of faces and automobiles.

Codes for nose might be emphasized in the face classification, both eye and mouth, wheel and window may be highlighted as codewords in the automobile subcategory.

Provided a library of training data, The classifier is trained to produce new distributions for each category.

The determination of classification is made by

c^{*}=\arg \max _{c}p(c|\mathbf {w} )=\arg \max _{c}p(c)p(\mathbf {w} |c)=\arg \max _{c}p(c)\prod _{n=1}^{N}p(w_{n}|c)

Since the Naïve Bayes classifier is simple yet effective, It's the standard by which all other comparisons are made.

The basic assumption of Naïve Bayes model does not hold sometimes.

For example, Multiple concepts can be depicted in a single photograph of a natural setting.

Two well-known topic models in the textual domain that take on the related multiple theme problem are probabilistic latent semantic analysis (pLSA) and topic modeling.

To illustrate, consider LDA.

LDA image modeling for natural scenes, comparison to the study of documents:

There is a correspondence between the categories of images and documents; Similar to how a random sampling of topics maps to a random sampling of themes,; Index topics correspond to those in the thematic index; The secret word is equivalent to the word.

On 13 different types of natural scenes, this method has proven to be very effective.

Due to the BoW model's use in image representation,, Text document classification can be attempted with any discriminative model, examples include support vector machines (SVM) If you're using a classifier that's based on the kernel, you can still use the kernel trick, the SVM system.

The Pyramid Match Kernel is a State-of-the-Art Implementation of the BoW Algorithm.

Using a BoW model representation learned by machine learning classifiers with varying kernels (e.g., a decision tree) is an example of the local feature approach, EMD-kernel and X^{2} kernel) has been vastly tested in the area of texture and object recognition.

Reports of very encouraging performance on various datasets have surfaced.

In the PASCAL Visual Object Classes Challenge, this method performed exceptionally well.

Pyramid match kernel

BoW's inability to account for spatial relationships between patches is a major shortcoming because they are crucial when depicting an image. Several approaches have been proposed by researchers to incorporate the spatial data. Correlogram features can improve feature quality by identifying spatial co-occurrences of features. method that incorporates locational details into the BoW framework.

The BoW model's performance is unclear because it has not been subjected to rigorous testing for view point invariance and scale invariance. Object segmentation and localization