Online Dictionary Learning Algorithms
Implementation of online dictionary learning algorithms for processing high-dimensional feature data, with comprehensive code examples and optimization techniques.
Semi-Supervised Image Classification Using Support Vector Machines
This approach implements semi-supervised image classification using Support Vector Machines (SVM). The method from literature [1] performs supervised image feature learning from all available data (both labeled and unlabeled samples). It leverages rich categorical attributes of images to automatically generate prototype set collections from existing data. Feature learning is then applied to each prototype set, and the projected features are concatenated to form comprehensive image representations for classification. The performance is compared with traditional semi-supervised methods that determine classification boundaries based solely on unlabeled images.
Deep Learning Generally Follows a Hierarchical Learning Structure
Deep learning typically adopts a hierarchical learning structure, which is theoretically grounded in simulating the workings of the human brain's cerebral cortex. The visual region of the cerebral cortex also operates hierarchically, with lower-level visual cortices being more sensitive to basic features. Consequently, feature learning is driven by numerous application demands and supported by biological neural theories, ensuring its significant role in the AI field. Some experiments indicate that features learned by certain feature learning methods often outperform other features; for instance, the ISA model discussed in this article is one such example. In code implementations, these hierarchical structures are often realized through stacked layers (e.g., convolutional, pooling, or fully connected layers), with activation functions like ReLU facilitating feature extraction at different abstraction levels.
Integral Image Computation with Haar Features and AdaBoost Algorithm Implementation
MATLAB code for integral image calculation, featuring Haar-like feature extraction and an AdaBoost classifier learner. This implementation provides an excellent educational resource for understanding computer vision algorithms with practical code examples.