Clustering techniques have been widely used in areas that handle massive amounts of data, such as statistics, information retrieval, data mining and image analysis.
An increasing volume of digital images and videos has become available over the years due to the growth of smartphones, tablets and Internet of Things in general. Therefore, the development of techniques capable of managing large amount of data in a fast and accurate way is important to extract any valuable information.
Finding natural groupings is the goal of clustering methods, such as K-means. They can help classify and separate information in order to make data analysis easier. Examples of problems related to data grouping are data indexing, data compression and natural image classification. Image Grouping can also be used to find discriminative visual concepts which can be used as mid-level features.
|1.||(2015): Partial Least Squares Image Clustering. In: Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 1-8, 2015.|