"Feature engineering or feature extraction or feature discovery is the process of extracting features (characteristics, properties, attributes) from raw data."
The process of identifying and capturing important points or attributes of an image that can be used to represent it in a more compact form.
Image Preprocessing: This involves applying various filters and techniques to improve the quality of the image, such as smoothing, enhancing contrast, and removing noise.
Feature Detection: This involves identifying specific structures and patterns in an image, such as corners, edges, and blobs.
Feature Description: This involves creating a descriptor or set of descriptors for each detected feature, which can be used to recognize similar features in other images.
Feature Matching: This involves finding correspondences between features in two or more images, which can be used for tasks such as image alignment and object recognition.
Scale-Invariant Feature Transform (SIFT): A popular feature extraction method that is robust to changes in scale, rotation, and illumination.
Speeded-Up Robust Feature (SURF): Another popular feature extraction method that is robust to changes in scale, rotation, and illumination, while being faster than SIFT.
Local Binary Patterns (LBP): A simple yet effective feature extraction method that can be used for tasks such as facial recognition and texture classification.
Convolutional Neural Networks (CNNs): A type of deep learning algorithm that is used for tasks such as object detection and image classification, and which can automatically learn features from images.
Principal Component Analysis (PCA): A statistical technique for reducing the dimensionality of feature vectors, while preserving as much information as possible.
Histogram of Oriented Gradients (HOG): A feature extraction method that is commonly used for tasks such as pedestrian detection and image segmentation.
Gabor Filters: A type of wavelet transform that can be used for tasks such as feature detection and texture analysis.
Discrete Wavelet Transform (DWT): A mathematical technique for decomposing an image into its frequency components, which can be used for tasks such as compression and feature extraction.
Independent Component Analysis (ICA): A statistical technique for separating an image into its underlying components, which can be used for tasks such as image separation and feature extraction.
Scale Space Representation: A technique for representing an image at different scales, which can be used for tasks such as edge detection and texture analysis.
Bag of Visual Words model: A type of feature extraction method used in image classification, which involves partitioning an image into small regions and creating a histogram of the visual words representing features in each region.
Local Phase Quantization (LPQ): A feature extraction method that is robust to changes in illumination, and can be used for tasks such as facial recognition and texture classification.
Scale-Invariant Feature Transform with Accelerated Segment Test (SIFT-AS): A variant of SIFT that is faster and more accurate, particularly for large-scale images.
Histogram of Oriented Gradient Descriptor (HOGD): A variant of HOG that uses gradient orientation histograms to describe features in an image.
Local Binary Pattern Variance (LBPV): A feature extraction method that is particularly useful for image texture analysis, and which can be combined with other methods such as SIFT and HOG.
Multi-scale Retinex (MSR): A feature extraction method that is particularly useful for enhancing low-contrast images, and which can be combined with other methods such as SIFT and HOG.
Color Histogram: Extracting histograms of the color distribution of an image.
Edge Detection: Identifying edges, or sharp changes in pixel intensities, in digital images.
Haar-Like Features: Identifying rectangular regions with different levels of intensity in an image.
Texton: Identifying commonly reoccurring visual patterns in an image.
Sift: Detecting and describing local features in an image.
HOG: Histogram of Oriented Gradients identifies edge directions in an image using gradient information.
Gabor Filters: Detecting texture variations across the different scale and orientations present in a digital image.
Scale-Invariant Feature Transform (SIFT): Able to detect scale and rotationally invariant features in an image.
Bag-of-Visual Words (BoVW): Performs image-to-image similarity comparisons by creating vectors from the frequency distribution of visual words extracted from image patches.
Fisher Vectors: A representation of an image by its gradients, and the distribution of gradient components in a Gaussian Mixture Model (GMM).
Convolutional Neural Networks (CNNs): One of the most popular deep learning methods applied to computer vision tasks, CNNs use a deep architecture to automatically learn features from input images.
Local Binary Patterns (LBP): A simple yet efficient texture feature extraction algorithm that describes the distribution of texture patterns of an image.
Speeded Up Robust Feature (SURF): An extension of SIFT, SURF also detects similar invariant local features in an image across different scales and rotations.
Deep Learning models like ResNet, VGG-16, etc.: These are models trained on large labeled datasets with deep architectures that can learn meaningful representations of images by stacking Convolutional, Pooling and Fully Connected layers.
"Due to deep learning networks, such as convolutional neural networks, that are able to learn it by itself, domain-specific-based feature engineering has become obsolete for vision and speech processing."
"Vision and speech processing."
"The process of extracting features (characteristics, properties, attributes) from raw data."
"Yes, deep learning networks can learn features by themselves."
"Examples of features in physics include the construction of dimensionless numbers such as Reynolds number in fluid dynamics; then Nusselt number in heat transfer; Archimedes number in sedimentation."
"Heat transfer."
"The construction of dimensionless numbers is used for approximations of the solution."
"Analytical strength of materials solutions in mechanics."
"Domain-specific-based feature engineering has become obsolete for vision and speech processing."
"No, feature engineering is still relevant in certain domains, such as physics."
"Due to deep learning networks, such as convolutional neural networks, that are able to learn it by itself, domain-specific-based feature engineering has become obsolete for vision and speech processing."
"Deep learning networks are able to learn features by themselves."
"Yes, feature engineering is applicable to various fields."
"The construction of dimensionless numbers, such as Reynolds number, in fluid dynamics."
"The Archimedes number is significant in sedimentation."
"Feature engineering is important in physics for constructing dimensionless numbers and approximations of solutions."
"Yes, deep learning networks are not sufficient for all domains and tasks."
"Deep learning networks, such as convolutional neural networks."
"Yes, feature engineering can assist in constructing first approximations of the solution, such as analytical strength of materials solutions in mechanics."