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Dataset Summarization by K Principal Concepts

8 Apr 2021arXiv:2104.03952archive 2025-07-28

Niv Cohen, Yedid Hoshen

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.

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Tasks

ClusteringImage Clustering

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Clustering CIFAR-10 Single-Noun Prior ARI 0.702 #21 of 40 Archive leaderboard report
Image Clustering CIFAR-10 Single-Noun Prior Accuracy 0.853 #21 of 40 Archive leaderboard report
Image Clustering CIFAR-10 Single-Noun Prior Backbone ViT-B-32 #21 of 40 Archive leaderboard report
Image Clustering CIFAR-10 Single-Noun Prior NMI 0.731 #21 of 40 Archive leaderboard report
Image Clustering CIFAR-10 Single-Noun Prior Train set Train+Test #21 of 40 Archive leaderboard report
Image Clustering ImageNet-100 (TEMI Split) Single-Noun Prior ACCURACY 0.731 #4 of 5 Archive leaderboard report
Image Clustering ImageNet-100 (TEMI Split) Single-Noun Prior ARI 0.628 #4 of 5 Archive leaderboard report
Image Clustering ImageNet-100 (TEMI Split) Single-Noun Prior NMI 0.805 #4 of 5 Archive leaderboard report
Image Clustering ImageNet-200 Single-Noun Prior ACCURACY 0.598 #5 of 5 Archive leaderboard report
Image Clustering ImageNet-200 Single-Noun Prior ARI 0.486 #5 of 5 Archive leaderboard report
Image Clustering ImageNet-200 Single-Noun Prior NMI 0.749 #5 of 5 Archive leaderboard report
Image Clustering ImageNet-50 (TEMI Split) Single-Noun Prior ACCURACY 0.827 #4 of 5 Archive leaderboard report
Image Clustering ImageNet-50 (TEMI Split) Single-Noun Prior ARI 0.744 #4 of 5 Archive leaderboard report
Image Clustering ImageNet-50 (TEMI Split) Single-Noun Prior NMI 0.847 #4 of 5 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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