  We propose the new task of K principal concept identification for dataset
summarizarion. The objective is to find a set of K concepts that best explain
the variation within the dataset. Concepts are high-level human interpretable
terms such as "tiger", "kayaking" or "happy". The K concepts are selected from
a (potentially long) input list of candidates, which we denote the
concept-bank. The concept-bank may be taken from a generic dictionary or
constructed by task-specific prior knowledge. An image-language embedding
method (e.g. CLIP) is used to map the images and the concept-bank into a shared
feature space. To select the K concepts that best explain the data, we
formulate our problem as a K-uncapacitated facility location problem. An
efficient optimization technique is used to scale the local search algorithm to
very large concept-banks. The output of our method is a set of K principal
concepts that summarize the dataset. Our approach provides a more explicit
summary in comparison to selecting K representative images, which are often
ambiguous. As a further application of our method, the K principal concepts can
be used to classify the dataset into K groups. Extensive experiments
demonstrate the efficacy of our approach.
