Imageswhether

Imageswhether is a term used in computer vision to describe the task of determining, for a given image, whether a specified binary attribute or condition is present. It blends "image" with "whether" to emphasize the boolean decision about the image's content. The concept is used to frame detectors that output a yes/no decision or a calibrated probability indicating the presence of a predicate rather than identifying specific objects or scenes.

In practice, imageswhether can be implemented as image-level predicate detection: a model assigns a score indicating the likelihood that the image satisfies the target predicate, such as "contains a person wearing a hat" or "image captured indoors." This differs from traditional object detection, which localizes instances of categories, whereas imageswhether focuses on global presence or absence of a property.

Approaches rely on neural networks trained with labeled examples; can also be integrated into multi-label classification or visual question answering pipelines that resolve a binary question about the image.

Applications include content moderation, image search filtering, dataset curation, and accessibility. Example: a search engine might use an imageswhether filter to retrieve only images that likely show "outdoor scenes" or "no people."

Challenges for imageswhether include defining predicates, threshold calibration, bias, and evaluation metrics such as AUROC and calibration curves. The term is not widely standardized and may have overlapping meanings with image-level attribute detection or binary classifiers within broader vision systems.

See also: image classification, predicate detection, visual question answering, content moderation.