Semantic Segmentation Tree for image content representation

Semantic Segmentation Tree for Image Content Representation

In this paper, we present a novel algorithm for representing image content by constructing a hierarchy of semantic image regions, called a Semantic Segmentation Tree (SSeg-tree). First, the hill-manipulation algorithm divides an image into several visually coherent segments (small regions), which form the leaves of the SSeg-tree. Then, the method groups these segments based on well-defined spat...

Multilevel image segmentation and object representation for content-based image retrieval

Semantic Image Segmentation

Machine learning methods in general and Deep Neural Networks in particular have shown to be vulnerable to adversarial perturbations. So far this phenomenon has mainly been studied in the context of whole-image classification. In this contribution, we analyse how adversarial perturbations can affect the task of semantic segmentation. We show how existing adversarial attackers can be transferred ...

Semantic Representation of Multimedia Content

Multimedia documents constitute extremely rich information resources, whose efficacious management is intertwined with the effective capturing of the underlying semantics. The conveyed meaning may span along multiple levels and relates to search and retrieval tasks, much as to the very extraction and interpretation of content descriptions. In this chapter we consider the formal representation o...

Tree-Cut for Probabilistic Image Segmentation

This paper presents a new probabilistic generative model for image segmentation, i.e. the task of partitioning an image into homogeneous regions. Our model is grounded on a mid-level image representation, called a region tree, in which regions are recursively split into subregions until superpixels are reached. Given the region tree, image segmentation is formalized as sampling cuts in the tree...