TL;DR: The paper proposes a new method called Image Clustering Conditioned on Text Criteria (IC|TC) for clustering images based on user-specified text criteria. The key ideas are: User provides a natural language text describing the desired clustering criterion (e.g. "cluster by action"). A...
Classic metrics used in clustering literature. python ictc/measuring_acc.py --dataset cifar10 📌 Citation If you feel IC|TC useful for your research and applications, please cite using this BibTeX: @article{ kwon2024ictc, title={Image Clustering Conditioned on Text Criteria}, author={Sehyun ...
Image quantization is the process of reducing the number of distinct colors or levels of intensity in a digital image, aiming to minimize distortion criteria while converting pixels to a smaller set of output levels. AI generated definition based on: Handbook of Image and Video Processing (Second...
A simple method of creating concept weights is to perform K-means clustering on the text features of the queries in the test set. Each cluster center becomes its own concept to learn. The concept weights U are then encoded as one-hot cluster membership vectors which we found to work better...
Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66, 846–850 (1971). Article Google Scholar Meilă, M. Comparing clusterings by the variation of information. In Learning Theory and Kernel Machines 173–187 (Springer, 2003). Côté, M. A. et al. ...
Olfa Limam and Fouad Ben Abdelaziz. Multicriteria fuzzy clustering for brain image segmentation. 2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), pages 1019 - 1031, 2013.Limam, O.; Ben Abdelaziz, F., "Multicriteria fuzzy clustering for brain image ...
Fast response: Responds to queries in milliseconds based on ultra-large-scale clustering and quantization-based indexing. Large amounts of data: Scans tens of billions of images for each search based on the large-scale search engine. API operations: Allows you to call API operations to add or...
Moreover, we suspect that such a network will struggle with coarse characteristics which are very dissimilar when observed at a fine scale, but very similar on a coarse scale, as the coarse scale analysis is conditioned on the fine scale analysis. Therefore, we expect that a single very ...
In the clustering process, we eliminate the local outliers and optimize the similarity evaluation criteria to improve the boundary adherence performance. In order to reduce the leakage of superpixels, a contour constraint is applied simultaneously to the similarity measure function to refine the initial...
3.2.3. Constraints Based on Neighborhood Spatial Information In the previous introduction, we obtain the pseudo labels by clustering constraints. In this section, considering the fact that neighboring pixels in the hyperspectral image space domain likely belong to the same class, we introduce a local...