An adaptive version of k-medoids to deal with the uncertainty in clustering heterogeneous data using an intermediary fusion approach

This paper introduces Hk-medoids, a modified version of the standard k-medoids algorithm. The modification extends the algorithm for the problem of clustering complex heterogeneous objects that are described by a diversity of data types, e.g. text, images, structured data and time series. We first proposed an intermediary fusion approach to calculate fused similarities between objects, SMF, taking into account the similarities between the component elements of the objects using appropriate similarity measures. The fused approach entails uncertainty for incomplete objects or for objects which have diverging distances according to the different component. Our implementation of Hk-medoids proposed here works with the fused distances and deals with the uncertainty in the fusion process. We experimentally evaluate the potential of our proposed algorithm using five datasets with different combinations of data types that define the objects. Our results show the feasibility of the our algorithm, and also they show a performance enhancement when comparing to the application of the original SMF approach in combination with a standard k-medoids that does not take uncertainty into account. In addition, from a theoretical point of view, our proposed algorithm has lower computation complexity than the popular PAM implementation

The effect of different bands of LED exposure on vitreous metabolites and retinal architecture.

Abstract The accumulating experimental evidence indicates that exposure to blue and white LED light leads to damage in the visual system against short-term exposure. Chronic exposure, adaptive responses to light and self-protective mechanisms against LED light exposures need to be explored and would be essential to know the repercussions of LED radiations on vitreous metabolites. A total of 24 male Wistar rats were used in our study, which was divided into four groups (n = 6 in each group). Three groups were exposed to either blue, white, or yellow LED light for 90 days (12:12 light-dark cycle routine) with uniform illumination (450–500 lux). Control rats were maintained under standard laboratory conditions. Post-exposure the vitreous was removed for mass spectrometry and retinal tissues for immunofluorescence and H&amp;E staining. The thickness of the retina decreased in blue and white light exposure animals compared with controls, whereas the yellow light exposure group showed an increase in thickness (p &lt; 0.001). The number of apoptotic cells was significantly lower in controls compared to light-exposed groups (p &lt; 0.001) and (p &lt; 0.001). Altered metabolites were observed in light exposure groups particularly in D-alanine, taurine, D-serin (p &lt; 0.05 and lysine (p &lt; 0.001). The self-protective or reworking system in the chronic light exposure could be dazed and drop the ability to compensate for the defending mechanism. This might fail to maintain the metabolomic structural integrity of the vitreous and retina.</jats:p

COSFIRE : A Brain-Inspired Approach to Visual Pattern Recognition

The primate visual system has an impressive ability to generalize and to discriminate between numerous objects and it is robust to many geometrical transformations as well as lighting conditions. The study of the visual system has been an active reasearch field in neuropysiology for more than half a century. The construction of computational models of visual neurons can help us gain insight in the processing of information in visual cortex which we can use to provide more robust solutions to computer vision applications. Here, we demonstrate how inspiration from the functions of shape-selective V4 neurons can be used to design trainable filters for visual pattern recognition. We call this approach COSFIRE, which stands for Combination of Shifted Filter Responses. We illustrate how a COSFIRE filter can be configured to be selective for the spatial arrangement of lines and/or edges that form the shape of a given prototype pattern. Finally, we demonstrate the effectiveness of the COSFIRE approach in three applications: the detection of vascular bifurcations in retinal fundus images, the localization and recognition of traffic signs in complex scenes and the recognition of handwritten digits. This work is a further step in understanding how visual information is processed in the brain and how information on pixel intensities is converted into information about objects. We demonstrate how this understanding can be used for the design of effective computer vision algorithms