Pattern Recognition and Classification Techniques

• Pattern Recognition is used to identify and categorize objects, patterns, or data into predefined categories.
• Typically, the process involves extracting features from input data, then using machine learning algorithms to map the extracted features to the appropriate class or label.
• Recognition and classification are fundamental in computer vision, natural language processing, healthcare, finance, and security.
• These recognition techniques allow for automated decision-making, pattern recognition, and data analysis.
• Image classification is one example where images are assigned to predefined categories like "cat", "dog", or "flower."
• Handwritten digit recognition identifies digits from handwritten input.
• Another application is spam detection, where emails are sorted as "spam" or "not spam."
• Sentiment analysis determines the sentiment, or emotional intention (positive, negative, or neutral) of text data.

Feature Extraction

• Feature extraction is important in data processing in computer vision and natural language processing.
• Raw data like images, videos, and text tends to contain vast amounts of information.
• Image is made up of million of pixels.
• Processing a high volume of data is often time-consuming and expensive.
• Feature extraction aims to reduce that amount of data for processing.

Feature Extraction Benefits

• Accuracy and efficiency of machine learning models can be improved and overfitting is avoided.
• Reduced data processing and model training leads to faster processing and lower use of computing resources.
• Understanding the data is improved which leads to the ability to identify factors behind a studied phenomenon.
• Common feature algorithms include dimensionality reduction like principal component analysis, linear discriminant analysis, image processing like Local Binary Patterns (LBP), Gray Level Co-occurrence Matrix (GLCM), and natural language processing such as bag-of-words, and word embeddings.
• The type of data and the specific problem will determine the feature extraction technique.

Local Binary Pattern (LBP)

• LBP extracts texture features in images, making it useful for image classification, facial recognition, and lesion detection.
• The LBP is applied to find important features related to texture.

How LBP Works

• LBP works by focusing on a central pixel, labeled gc, in the image.
• The central pixel gets compared with its neighboring pixels, labeled gp, in a defined area.
• The central pixel's gray level is what get's compared to that of its neighboring pixels.
• If central pixel's gray level is greater than or equal to it's neighbors, it is assigned a value of 1.
• If central pixel's gray level is less than it's neighbors, it is assigned a value of 0.

Local Texture Pattern Generation

• Central pixel values compared to its neighboring pixels gives you a binary code.
• Then, the binary code is turned into a decimal to find the texture around the central pixel.
• The comparison and binary encoding happen for every pixel in the image in each of the surrounding neighborhoods.
• A binary code from the center pixel and its surrounding pixels represents the Local Texture Pattern.
• The set of binary codes generated for the pixels give the overall texture of the image.
• A neighborhood size like 3x3 or 4x4 defines how many surrounding pixels are thought about for each central pixel.
• The radius (R) defines how far the neighborhood reaches from the central pixel with one pixel for a 3x3 neighborhood.

Local Binary Pattern (LBP) Equation

• LBP (ХС, Ус)R,P = Σ s((gp-gc). 2p
• gc is the central or target pixel.
• gp is the gray level in the neighborhoods that surround the central pixel.
• R defines how far the neighbors are from the central pixel
• If R=1, the neighbors are one pixel away or in a 3x3 neighborhood.
• If R=2, the neighbors are two pixels away which forms a larger neighborhood.
• P is the number neighbors and a 3x3 neighborhood has a P=8, while a 5x5 neighborhood has a P=16.
• The function s((gp - gc) is a compare function that will return 1 if gp ≥ gc, and 0 if gp <g c.
• The term 2^ p places a weighted value to the neighboring pixel relative to the central pixel.
• Effective feature extraction is important for the success of machine learning models.
• Feature extraction can help reduce data down to its most important elements.

Feature Extraction Examples

• Extracting features like edges, textures, and shapes from images is one example of image recognition.
• Extracting things like pitch, energy, and spectral information from audio signals is one example of speech recognition.

Models

• Models are mathematical representations or algorithms that predict outcomes based on input data.
• They learn patterns from training data and generalize these patterns to predict new data.
• Common techniques include supervised learning like logistic regression, decision trees, support vector machines and unsupervised learning like k-means clustering, and hierarchical clustering.
• Deep learning models such as CNNs and RNNs, are becoming popular for recognition and classification tasks, especially in computer vision and natural language processing.
• The choice of model depends on the problem size and complexity of the data and the desired level of interpretability and performance.
• Deep learning models and Machine Learning models in general enable many intelligent systems and apps that allow for automated decision-making.
• The right model and tuning of hyperparameters can impact a model's performance and generalization capabilities.
• Sample space division helps manage complexity and improve performance of machine learning algorithms.
• K-Fold Cross-Validation divides the data into the k subsets to be test sets while the remainder is used as training sets.
• Random Splitting randomly divides the data into training and test sets.

Explore pattern recognition and classification methods for categorizing data. Learn about feature extraction and machine learning algorithms used to map features to classes. Applications span computer vision, NLP, healthcare, finance, and security, enabling automated decision-making and data analysis.