Bildklassifizierungsalgorithmen

Bildklassifizierung is a German term that translates to "image classification" in English. It refers to the process of assigning a label or category to an image based on its visual content. This is a fundamental task in computer vision and artificial intelligence, with a wide range of applications.

The core idea behind image classification is to train a model, often a deep neural network, to recognize patterns and features within an image that are characteristic of specific classes. These classes can be very broad, such as "animal," "vehicle," or "building," or more specific, like "dog," "cat," "car," or "house." The training process involves feeding the model a large dataset of labeled images. The model learns to associate visual features with their corresponding labels.

Once trained, an image classification model can be used to predict the class of new, unseen images. This is achieved by analyzing the image and determining which learned features are most prominent. The model then outputs a probability distribution over the possible classes, indicating its confidence in each prediction.

Applications of image classification are extensive. They include content-based image retrieval, where users can search for images using keywords that describe their content. It is also crucial for autonomous driving systems, enabling vehicles to identify objects like pedestrians, other cars, and traffic signs. Medical imaging uses image classification to detect diseases and anomalies. Furthermore, it plays a role in security systems for facial recognition and in e-commerce for product categorization.