Manual inspection of images for classification, along with the definition of rules or patterns, is common in various real-world applications. Here are some practical examples:
- Medical image classification:
- Example: Classifying X-ray or MRI images as “normal” or “abnormal.”
- Rules/patterns: Radiologists visually inspect images for abnormalities, such as tumors, fractures, or anomalies in anatomy. Rules can be based on the presence, size, or location of these features.
- Plant disease detection:
- Example: Identifying plant diseases from images of leaves.
- Rules/patterns: Agricultural experts visually inspect leaf images for discoloration, spots, or unusual patterns. Rules can be defined based on the appearance and location of symptoms.
- Food quality inspection:
- Example: Classifying food products as “fresh” or “spoiled” from images.
- Rules/patterns: Food inspectors visually inspect images of fruits, vegetables, or packaged goods for signs of spoilage, mold, or other quality issues. Rules can be based on color, texture, or shape.
- Defect detection in manufacturing:
- Example: Detecting defects in manufactured products from images.
- Rules/patterns: Quality control inspectors visually inspect images of products for defects such as cracks, scratches, or missing components. Rules can be defined based on the location and characteristics of defects.
- Traffic sign recognition:
- Example: Recognizing traffic signs from images captured by autonomous vehicles.
- Rules/patterns: Engineers visually inspect images for the presence of signs and their shapes, colors, and symbols. Rules can be defined based on these visual cues.
- Wildlife monitoring:
- Example: Identifying and tracking animals in camera trap images.
- Rules/patterns: Wildlife experts visually inspect images for the presence of specific animal species, their behavior, or the time of day. Rules can be based on the appearance and context of animals.
- Historical document classification:
- Example: Classifying historical documents based on content or era.
- Rules/patterns: Archivists visually inspect scanned documents for handwriting style, language, content, or visual elements such as illustrations. Rules can be defined based on these characteristics.
- Security and surveillance:
- Example: Identifying security threats or intruders in surveillance camera footage.
- Rules/patterns: Security personnel visually inspect video feeds for unusual behavior, suspicious objects, or unauthorized access. Rules can be defined based on these observations.
In all these examples, experts or human annotators visually examine images, identify relevant patterns or features, and define rules or criteria for classification. These rules are often based on domain knowledge and experience. Once established, the rules can be used to create LFs and classify images automatically, assist in decision-making, or prioritize further analysis.