Color description is a challenging task because of large variations in RGB values which occur due to scene accidental events, such as shadows, shading, specularities, illuminant color changes, and changes in viewing geometry. Traditionally, this challenge has been addressed by capturing the variations in physics-based models, and deriving invariants for the undesired variations. The drawback of this approach is that sets of distinguishable colors in the original color space are mapped to the same value in the photometric invariant space. This results in a drop of discriminative power of the color description. In this paper we take an information theoretic approach to color description. We cluster color values together based on their discriminative power in a classification problem. The clustering has the explicit objective to minimize the drop of mutual information of the final representation. We show that such a color description automatically learns a certain degree of photometric invariance. We also show that a universal color representation, which is based on other data sets than the one at hand, can obtain competing performance. Experiments show that the proposed descriptor outperforms existing photometric invariants. Furthermore, we show that combined with shape description these color descriptors obtain excellent results on four challenging datasets, namely, PASCAL VOC 2007, Flowers-102, Stanford dogs-120 and Birds-200.

International audience

In this paper, we introduce two new morphological transforms for pattern matching in gray scale images. They rely on a profiling approach and are defined in the context of mathematical morphology. The first transform allows to detect all occurrences of a single pattern in an image, which justifies the name SOMP (single object matching using probing). It is shown to have the properties of a metric and therefore returns a measure of similarity between the search image and the reference pattern. Other properties relative to noise and computation time are highlighted. The second transform MOMP (multiple objects matching using probing) offers the ability to locate multiple patterns simultaneously. It is particularly suited to the detection of objects varying in size and with noisy distortion. Some results are presented for both transforms.