An Action Selection Architecture for an Emotional Agent

An architecture for action selection is presented linking emotion, cognition and behavior. It defines the information and emotion processes of an agent. The architecture has been implemented and used in a prototype environment

Bag-of-Colors for Biomedical Document Image Classification

The number of biomedical publications has increased noticeably in the last 30 years. Clinicians and medical researchers regularly have unmet information needs but require more time for searching than is usually available to find publications relevant to a clinical situation. The techniques described in this article are used to classify images from the biomedical open access literature into categories, which can potentially reduce the search time. Only the visual information of the images is used to classify images based on a benchmark database of ImageCLEF 2011 created for the task of image classification and image retrieval. We evaluate particularly the importance of color in addition to the frequently used texture and grey level features. Results show that bags–of–colors in combination with the Scale Invariant Feature Transform (SIFT) provide an image representation allowing to improve the classification quality. Accuracy improved from 69.75% of the best system in ImageCLEF 2011 using visual information, only, to 72.5% of the system described in this paper. The results highlight the importance of color for the classification of biomedical images

Fast and Accurate Texture Recognition with Multilayer Convolution and Multifractal Analysis

International audienceA fast and accurate texture recognition system is presented. The new approach consists in extracting locally and globally invariant representations. The locally invariant representation is built on a multi-resolution convolutional net- work with a local pooling operator to improve robustness to local orientation and scale changes. This representation is mapped into a globally invariant descriptor using multifractal analysis. We propose a new multifractal descriptor that cap- tures rich texture information and is mathematically invariant to various complex transformations. In addition, two more techniques are presented to further im- prove the robustness of our system. The first technique consists in combining the generative PCA classifier with multiclass SVMs. The second technique consists of two simple strategies to boost classification results by synthetically augment- ing the training set. Experiments show that the proposed solution outperforms existing methods on three challenging public benchmark datasets, while being computationally efficient

Material-specific adaptation of color invariant features

For the modeling of materials, the mapping of image features onto a codebook of feature representatives receives extensive treatment. For reason of their generality and simplicity, filterbank outputs are commonly used as features. The MR8 filterbank of Varma and Zisserman is performing well in a recent evaluation. In this paper, we construct color invariant filter sets from the original MR8 filterbank. We evaluate several color invariant alternatives over more than 250 real-world materials recorded under a variety of imaging conditions including clutter. Our contribution is a material recognition framework that learns automatically for each material specifically the most discriminative filterbank combination and corresponding degree of color invariance. For a large set of materials each with different physical properties, we demonstrate the material-specific filterbank models to be preferred over models with fixed filterbanks

Color Textons for Texture Recognition

Texton models have proven to be very discriminative for the recognition of grayvalue images taken from rough textures. To further improve the discriminative power of the distinctive texton models of Varma and Zisserman (VZ model) (IJCV, vol. 62(1), pp. 61-81, 2005), we propose two schemes to exploit color information. First, we incorporate color information directly at the texton level, and apply color invariants to deal with straightforward illumination effects as local intensity, shading and shadow. But, the learning of representatives of the spatial structure and colors of textures may be hampered by the wide variety of apparent structure-color combinations. Therefore, our second contribution is an alternative approach, where we weight grayvalue-based textons with color information in a post-processing step, leaving the original VZ algorithm intact. We demonstrate that the color-weighted textons outperform the VZ textons as well as the color invariant textons. The color-weighted textons are specifically more discriminative than grayvalue-based textons when the size of the example image set is reduced. When using 2 example images only, recognition performance is 85.6%, which is an improvement over grayvaluebased textons of 10%. Hence, incorporating color in textons facilitates the learning of textons