Knowledge-Based Classification Algorithms Knowledge-Based Classification Algorithms Knowledge-based classification algorithms refer to a category of machine learning algorithms that employ pre-existing knowledge or rules to classify novel instances or data points. The algorithms in question are dependent on a knowledge base, which is a compilation of established patterns, rules, or associations among attributes, for the purpose of predicting outcomes or assigning class designations to unobserved data. In contrast to conventional classification algorithms that acquire patterns or rules directly from the training data, knowledge-based classification algorithms utilise pre-existing knowledge or domain expertise to steer the classification procedure. Pre-existing knowledge can be sourced from human experts or obtained from pre-existing resources such as databases, ontologies, or expe...
What to do When You Don't Have Gold Standard Images to Compare Your Processed Images How to Measure the Quality of Images When You Don't Have Gold Standard Images to Compare In the realm of image analysis and processing, quantifying image quality forms a crucial cornerstone of various methodologies. This is especially true when there's a lack of gold standard images or ground truth to set the benchmark. In such scenarios, we're often left asking: how can we measure image quality objectively or subjectively? Let's delve into this topic with an emphasis on establishing robust and reliable metrics for image quality analysis. Objective Measurement of Image Quality Without Ground Truth Objective quality metrics quantify the difference between two images based on numerical methods. But, how do we assess image quality objectivel...