30,182 research outputs found
Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches
In this work, we evaluate two different image clustering objectives, k-means
clustering and correlation clustering, in the context of Triplet Loss induced
feature space embeddings. Specifically, we train a convolutional neural network
to learn discriminative features by optimizing two popular versions of the
Triplet Loss in order to study their clustering properties under the assumption
of noisy labels. Additionally, we propose a new, simple Triplet Loss
formulation, which shows desirable properties with respect to formal clustering
objectives and outperforms the existing methods. We evaluate all three Triplet
loss formulations for K-means and correlation clustering on the CIFAR-10 image
classification dataset
Recommended from our members
Clustering driving styles via image processing
It has become of key interest in the insurance industry to understand and extract information from telematics car driving data. Telematics car driving data of individual car drivers can be summarized in so-called speed acceleration heatmaps. The aim of this study is to cluster such speed acceleration heatmaps to different categories by analysing similarities and differences in these heatmaps. Making use of local smoothness properties, we propose to process these heatmaps as RGB images. Clustering can then be achieved by involving supervised information via a transfer learning approach using the pre-trained AlexNet to extract discriminative features. The K-means algorithm is then applied on these extracted discriminative features for clustering. The experiment results in an improvement of heatmap clustering compared to classical approaches
Supervised Spectral Subspace Clustering for Visual Dictionary Creation in the Context of Image Classification
International audienceWhen building traditional Bag of Visual Words (BOW) for image classification, the K-means algorithm is usually used on a large set of high dimensional local descriptors to build the visual dictionary. However, it is very likely that, to find a good visual vocabulary, only a sub-part of the descriptor space of each visual word is truly relevant. We propose a novel framework for creating the visual dictionary based on a spectral subspace clustering method instead of the traditional K-means algorithm. A strategy for adding supervised information during the subspace clustering process is formulated to obtain more discriminative visual words. Experimental results on real world image dataset show that the proposed framework for dictionary creation improves the classification accuracy compared to using traditionally built BOW
Interpretable Sequence Clustering
Categorical sequence clustering plays a crucial role in various fields, but
the lack of interpretability in cluster assignments poses significant
challenges. Sequences inherently lack explicit features, and existing sequence
clustering algorithms heavily rely on complex representations, making it
difficult to explain their results. To address this issue, we propose a method
called Interpretable Sequence Clustering Tree (ISCT), which combines sequential
patterns with a concise and interpretable tree structure. ISCT leverages k-1
patterns to generate k leaf nodes, corresponding to k clusters, which provides
an intuitive explanation on how each cluster is formed. More precisely, ISCT
first projects sequences into random subspaces and then utilizes the k-means
algorithm to obtain high-quality initial cluster assignments. Subsequently, it
constructs a pattern-based decision tree using a boosting-based construction
strategy in which sequences are re-projected and re-clustered at each node
before mining the top-1 discriminative splitting pattern. Experimental results
on 14 real-world data sets demonstrate that our proposed method provides an
interpretable tree structure while delivering fast and accurate cluster
assignments.Comment: 11 pages, 6 figure
- …