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Mapping of the Genome Sequence Using Two-stage Self Organizing Maps
Get PDFIn this paper, we introduce an algorithm of Self-Organizing Maps(SOM) which can map the genome sequence continuously on the map. The DNA sequences are considered to have the special features depending on the regions where the sequences are taken from or the gene functions of the proteins which are translated from the sequences. If the hidden features of the DNA sequences are extracted from the DNA sequences, they can be used for predicting the regions or the functions of the sequences. In this paper, we propose the algorithms using two stage SOM which organizes the sequences of the specific length at the first stage and organizes the set of sequences at the 2nd stage This algorithm can map the genome sequences on the map at each stage depending on the features of the sequences. We made some analyses of the genome sequences concerning the functions, species and secondary structure of the sequences
Visual mining in music collections with Emergent SOM
Get PDFDifferent methods of organizing large collections of music with databionic mining techniques are described. The Emergent Self-Organizing Map is used to cluster and visualize similar artists and songs. The first method is the MusicMiner system that utilizes semantic descriptions learned from low level audio features for each song. The second method uses tags that have been assigned to music artists by the users of the social music platform Last.fm. For both methods we demonstrate the visualization capabilities of the U-Map. An intuitive browsing of large music collections is offered based on the paradigm of topographic maps. The semantic concepts behind the features enhance the interpretability of the maps
Data fusion and eigenface based tracking dedicated to a Tour-Guide Robot
Get PDFThis article presents a key-scenario of H/R interaction for our tour-guide robot. Given this scenario, three visual modalities, the robot deals with, have been outlined, namely the "search of visitors" attending the exhibition, the "proximal interaction" through the robot interface and the "guidance mission". The paper focuses on the two last ones which involves face recognition and visual data fusion in a particle filtering framework. Evaluations on key-sequences in a human centred environment show the tracker robustness to background clutter, sporadic occlusions and group of persons. The tracker is able to cope with target loss by detecting and re-initializing automatically thanks to the face recognition outcome. Moreover, the multi-cues association proved to be more robust to clutter than any of the cues individually
Toward robust foveated wide field of view people detection
Get PDFWe present foveated vision system for rapid and robust person detection. The system consists of an omidirectional camera for people detection in a wide field of view and a pan-tilt camera that can focus on a particular location. Combining the information from both cameras leads to more reliable people detection. The people detection is based on fast human body part detectors and a probabilistic model of the spatial arrangement of the parts. The model can be extended to multiple cameras. The representation is robust to partial occlusions, part detector false alarms and missed detections of body parts. We also show how to use the fact that the persons walk on a known ground plane to increase the efficiency and reliability of the detection. The detection does not rely on static background and the system is suitable for mobile platforms
Class imaging of hyperspectral satellite remote sensing data using FLSOM
Get PDFWe propose an extension of the self-organizing map for supervised fuzzy classification learning, whereby uncertain (fuzzy) class information is also allowed for training data. The method is able to detect class similarities, which can be used for data vizualization. Applying a special functional metric, derived from of the L_p norms, we show the application of the method for classification and visualization of hyper-spectral data in satellite image remote sensing image analysis
Cluster Analysis using Spherical SOM
Get PDFA cluster analysis method is proposed in this paper. As benchmark data, the Fisher's iris and the Wine recognition data sets are used. As a result of the numerical experiment, a clustering method using the dendrogram yielded 97 % in accuracy. It is difficult to display a multi-dimensional data by the dendrogram in one dimension. The ultimate visualization is by means of 3 dimensional rendition. We conclude that the best way that a multi-dimensional data set is visualized is by a sphere, since the phase relationship of it is smooth everywhere
Description of Input Patterns by Linear Mixtures of SOM Models
Get PDFThis paper introduces a novel way of analyzing input patterns presented to the Self-Organizing Map (SOM). Instead of identifying only the "winner," i.e., the model that matches best with the input, we determine the linear mixture of the models (reference vectors) of the SOM that approximates to the input vector best. It will be shown that if only nonnegative weights are allowed in this linear mixture, the expansion of the input pattern in terms of the models is very meaningful, contains only few terms, and provides a better insight into the input state than what the mere "winner" can give. If then the models fall into classes that are known a priori, the sums of the weights over each class can be interpreted as expressing the affiliation of the input with the due classes
Improving the H2MLVQ algorithm by the Cross Entropy Method
Get PDFThis paper addresses the use of a stochastic optimization method called the Cross Entropy (CE) Method in the improvement of a recently proposed H2MLVQ (Harmonic to minimum LVQ) algorithm, this algorithm was proposed as an initialization insensitive variant of the well known Learning Vector Quantization (LVQ) algorithm. This paper has two aims, the first aim is the use of the Cross Entropy (CE) Method to tackle the initialization sensitiveness problem associated with the original (LVQ) algorithm and its variants and the second aim is to use a weighted norm instead of the Euclidean norm in order to select the most relevant features. The results in this paper indicate that the CE method can successfully be applied to this kind of problems and efficiently generate high quality solutions. Also, good competitive numerical results on several datasets are reported
Attention and Visual Search : Active Robotic Vision Systems that Search
Get PDFVisual attention is a multi-faceted phenomenon, playing different roles in different situations and for different processing mechanisms. Regardless, attention is a mechanism that optimizes the search processes inherent in vision. This perspective leads to sound theoretical foundation for studies of attention in both machine and in the brain. The development of this foundation and the many ways in which attentional processes manifest themselves will be overviewed. One particular example of a practical robotic vision system that employs some of these attentional processes will be described. A difficult problem for robotic vision systems is visual search for a given target in an arbitrary 3D space. A solution to this problem will be described that optimizes the probability of finding the target given a fixed cost limit in terms of total number of robotic actions the robot requires to find its visual target. A robotic realization will be shown
Attentional Robot Localization and Mapping
Get PDFIn this paper, we introduce an application of visual attention in the field of robotics: attentional visual SLAM (Simultaneous Localization and Mapping). A biologically motivated attention system finds regions of interest which serve as visual landmarks for the robot. The regions are tracked and matched over consecutive frames to build stable landmarks and to estimate the 3D position of the landmarks in the environment. Furthermore, matching of current landmarks to database entries enables loop closing and global localization. Additionally, the system is equipped with an active camera control, which supports the system with a tracking, a re-detection, and an exploration behaviour