Image Classification and Regression Techniques

A binary string representing the comparison between a center pixel and its neighbors, with '1' indicating a neighbor pixel value greater than or equal to the center pixel and '0' otherwise.

An unsigned integer representation of the LBP code string, typically converted to a form that can be processed by computers.

An image where each pixel value is replaced by its corresponding LBP code.

The histogram of the LBP image, used to capture the distribution of different LBP codes within the image. It's an effective way to describe the texture of an image using LBP codes.

A technique to analyze the texture of an image where each pixel is characterized by its relationship to neighboring pixels.

A category of LBP codes with a simple transition pattern between neighboring pixels.

A category of LBP codes with complex or irregular transitions between neighboring pixels.

The process of analyzing the texture of an image using LBP codes, typically involving steps like LBP code generation, histogram calculation and feature vector extraction.

The process of adjusting a neural network's parameters (weights and biases) to make accurate predictions. This involves optimizing the network's outputs to match the desired results for a training dataset.

A function that measures the difference between predicted outputs and actual values. It helps the neural network learn by guiding the adjustments of its parameters to minimize this error.

Used for binary classification tasks, predicting one of two outcomes. It's based on a single output neuron with a Sigmoid activation function.

Used for multi-class classification tasks, predicting one of multiple outcomes. It involves two or more output neurons with a Softmax activation function.

Used for regression tasks, predicting continuous values (e.g., temperature, price). It measures the squared difference between predicted and actual values.

Used for regression tasks, predicting continuous values. It measures the absolute difference between predicted values and actual values.

A type of pooling layer in neural networks that takes the maximum value from a specified region of the input to reduce the dimensionality of the data while retaining the most important features.

The size of the window used in 2D Max Pooling to scan the input data.

The step size with which the window moves across the input. It controls the amount of overlap.

The process of reducing the dimensionality of the input by selecting the maximum value from each window.

The output of the Max Pooling layer, which has a reduced size compared to the input, retaining the most important feature information.

A neural network layer that processes spatial data by applying filters, called kernels, to extract features from the input. These filters are small matrices that slide across the input data, performing element-wise multiplications and summations to generate feature maps.

In a 2D Convolutional Layer, kernels are small matrices (often 3x3 or 5x5) that are applied to the input data to extract features. They slide across the input and perform element-wise multiplications and summations, producing a feature map representing the detected feature.

The number of channels in the input is denoted as the depth of the input tensor. This specifies the number of input feature maps that the convolutional layer processes.

The number of kernels used in the convolutional layer determines the number of output feature maps. Each kernel acts as a filter, extracting a specific feature from the input data.

The size of the kernel specifies the area covered by the filter when it slides across the input data. Smaller kernel sizes (e.g., 3x3) help capture local features, while larger kernel sizes detect larger patterns across the input.

During the convolution process, the kernel slides across the input data, multiplying its elements with the respective input values and summing the results. Each kernel generates a single output feature channel.

Padding is used to maintain the size of the output feature map by adding extra values (typically zeros) around the edges of the input. This helps prevent information loss and retain spatial dimensions.

The bias term is a constant value added to the output of the convolution operation. It helps adjust the activation function's output range. Essentially, biases allow the model to shift the activation function's output up or down, providing more flexibility in learning.

The ReLU (Rectified Linear Unit) function is a non-linear activation function often used in convolutional neural networks. It introduces non-linearity, which is crucial for learning complex patterns in data. It acts as a threshold, effectively 'turning off' neurons with negative output.

In convolutional neural networks, each kernel has its own unique bias term. This is essential because different kernels learn to detect different features, and the bias term allows each kernel to adjust its output independently.

The convolutional layer's input typically has multiple channels, corresponding to different aspects of the data. For instance, in images, the channels might represent red, green, and blue (RGB). The kernel's channels must match the input channels to extract the corresponding features.

A convolutional layer can have several kernels, not just one. This allows the network to extract multiple features from the input data. Think of it as learning from multiple perspectives.

A convolutional layer transforms an input by performing convolutions with multiple kernels. The output of this transformation is a multi-channel feature map, where each channel contains information extracted by a specific kernel.

The convolutional layer's output is a feature map, with each channel representing the features extracted by a specific kernel. This multi-channel feature map then gets processed further in the neural network.

A pre-trained model, like VGG16, trained on a massive dataset like ImageNet, with its final layers removed. Used as a starting point for specific tasks without training on a large dataset.

Pre-trained model layers, such as convolutional blocks, used to extract relevant features from images. These layers are often frozen during training to preserve their learned features.

Layers added to the pre-trained model, specifically trained for the target task. These layers adapt the base model to the specific problem. For example, a classifier for recognizing cats vs dogs.

A pre-trained convolutional neural network (CNN) architecture with a fixed structure consisting of a stack of convolutional layers.

The initial dataset used to train the pre-trained model.

The process where a model learned on one task is used to improve performance on a similar but different task.

The initial training phase on the ImageNet dataset.

Training the custom layers added to adapt the base model to the specific task.

The input image passed to the VGG16 model.

The data dimensions of the input image: 224 pixels in width and height, and 3 channels (red, green, blue).

The dimension of the final output from VGG16 before the custom layers.

The custom layer responsible for classifying images into different classes.

A mathematical function applied to the final outputs to generate probabilities for each class.

The final output of the model, representing the probabilities for each class.