… first find clusters of similar reflectance in the image following … boundaries between clusters
(as opposed to the whole image), … preserving reflectance between clusters even if they are not …

… Every reflectance component ri belongs to one of the clusters and we denote its cluster
membership with the variable αi ∈ {1,...,C}. This is summarized in the following energy term …

… directly uncovers the intrinsic discrimination of target data via constrained clustering, where
we … Technically, we propose a hybrid model of deep clustering that integrates a regularized …

… The maximum clustering rates are indicated in parenthesis. We observe that the clustering
rates of the intrinsic and extrinsic formulations are comparable but relatively lower than the …

… 3, theoretical basis of spectral clustering, intrinsic dimension and local tangent space are …
using intrinsic dimension and local tangent space, the manifold spectral clustering based on …

… We demonstrate intrinsic image decomposition on a variety of images and show … Figure 7:
For a downsampling factor p, we use K-means clustering to extract v2 representative colors of …

… with simulated transmission electron microscope images and a data set from a … intrinsic
classification of images with a common motif. We also discuss a mechanism for clustering images …

… also have some intrinsic limitations. First, multi-stage … the image clustering task as a binary
pairwise-classification problem to judge whether pairs of images belong to the same clusters. …

… so that lighting can be accounted for in image understanding. To simplify inference, methods
for … Most techniques perform this estimation on a single input image, as this allows for wider …

In this paper, we propose a new image clustering algorithm, referred to as clustering using …
the clustering performance of samples within the local clique. To obtain the clustering result, …