Category level object segmentation by combining bag-of-words models and Markov Random Fields

The introduced approach is evaluated for automatic diagnosis of normal and abnormal color retinal images with bright lesions such as drusen and exudates.. This approach has

The main contribution of this paper is a tractable model suitable for object segmen- tation, that takes advantage of two complementary com- ponents: (a) a model with MRF properties

The rgb color feature, used at the instance level, gives an improvement for two categories out of three. When an ob- ject is correctly localized, we observed that this color com-

We propose to use, for each point x k , two measures that overcome these drawbacks: a fuzzy overlap measure which evaluates the degree of overlap of a specified number (l) of

We assume that the matches observed in the valida- tion images correctly match the corresponding object parts and that the observed appearances a of the matches in the validation

Using these masks as part of the supervisory signal provides an object-level prior for the semantic correspondence task and offers a good compromise between semantic flow methods,

On the other, socio- cultural and historical parameters appear to be relevant at each level of color categorization and lexicalization, in the shaping of basic terms, in the

Based on a layer scanning algorithm, the color reordering generates gray level images and makes it possible to embed the color palette into the gray level images using a data