Distance Measures in the Training Phase of Self-Organizing Map for Color Histogram Generation in Spectral Image Retrieval

Kohonen, Oili; Hauta-Kasari, Markku

doi:10.2352/j.imagingsci.technol.(2008)52:2(020201)

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…The approach of k -means clustering of SOM has been successfully employed for the identification of distinct gene expression patterns [ 75 ]. For the training of SOM, we used a starting input grid of 24 neurons and 4000 iterations, while the Gauss neighborhood function [ 76 ] and the Euclidean distance measure [ 77 ] were applied for updating the grid. Subsequently, the output of SOM was fed as input vector to the k -means clustering algorithm, in order to define the boarders between the nodes and to put in the same cluster nodes that were close to each other.…”

Section: Methodsmentioning

confidence: 99%

Deciphering the signaling mechanisms of the plant cell wall degradation machinery in Aspergillus oryzae

et al. 2015

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BackgroundThe gene expression and secretion of fungal lignocellulolytic enzymes are tightly controlled at the transcription level using independent mechanisms to respond to distinct inducers from plant biomass. An advanced systems-level understanding of transcriptional regulatory networks is required to rationally engineer filamentous fungi for more efficient bioconversion of different types of biomass.ResultsIn this study we focused on ten chemically defined inducers to drive expression of cellulases, hemicellulases and accessory enzymes in the model filamentous fungus Aspergillus oryzae and shed light on the complex network of transcriptional activators required. The chemical diversity analysis of the inducers, based on 186 chemical descriptors calculated from the structure, resulted into three clusters, however, the global, metabolic and extracellular protein transcription of the A. oryzae genome were only partially explained by the chemical similarity of the enzyme inducers. Genes encoding enzymes that have attracted considerable interest such as cellobiose dehydrogenases and copper-dependent polysaccharide mono-oxygenases presented a substrate-specific induction. Several homology-model structures were derived using ab-initio multiple threading alignment in our effort to elucidate the interplay of transcription factors involved in regulating plant-deconstructing enzymes and metabolites. Systematic investigation of metabolite-protein interactions, using the 814 unique reactants involved in 2360 reactions in the genome scale metabolic network of A. oryzae, was performed through a two-step molecular docking against the binding pockets of the transcription factors AoXlnR and AoAmyR. A total of six metabolites viz., sulfite (H2SO3), sulfate (SLF), uroporphyrinogen III (UPGIII), ethanolamine phosphate (PETHM), D-glyceraldehyde 3-phosphate (T3P1) and taurine (TAUR) were found as strong binders, whereas the genes involved in the metabolic reactions that these metabolites appear were found to be significantly differentially expressed when comparing the inducers with glucose.ConclusionsBased on our observations, we believe that specific binding of sulfite to the regulator of the cellulase gene expression, AoXlnR, may be the molecular basis for the connection of sulfur metabolism and cellulase gene expression in filamentous fungi. Further characterization and manipulation of the regulatory network components identified in this study, will enable rational engineering of industrial strains for improved production of the sophisticated set of enzymes necessary to break-down chemically divergent plant biomass.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0224-5) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Deciphering the signaling mechanisms of the plant cell wall degradation machinery in Aspergillus oryzae

et al. 2015

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“…The efficiency and performance of SOM compared to conventional statistical methods can be found in several review articles (Gevrey et al, 2003;Kalteh et al, 2008). The detailed inspiration and methodological aspects can be found in other computational papers (Kohonen, 1998;Kohonen and Hauta-Kasari, 2008).…”

Section: Multivariate Analysis Using Self-organizing Map (Som)mentioning

confidence: 99%

“…SOM modeling was conducted using Matlab 6.1 and the SOM coding solution based on the SOM Toolbox for Matlab (Alhoniemi et al, 2000). Further information on this modeling approach can be found in Kohonen (2000).…”

Section: Multivariate Analysis Using Self-organizing Map (Som)mentioning

confidence: 99%

Impact of summer rainfall on the seasonal water quality variation (chlorophyll a) in the regulated Nakdong River

et al. 2011

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In this study, the relationship between hydrological characteristics and phytoplankton biomass (chlorophyll a concentration) was explored using long-term hydrological and limnological data (1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007) in a flow-regulated river system (the lower Nakdong River, South Korea). This river has four multi-purpose dams and an estuarine barrage, and river flow is extensively controlled. Dynamics of chlorophyll a concentration responded well to changes in hydrology, such as rainfall, dam discharge and river flow. The hydrological characteristics strongly affected the decrease of chlorophyll a concentration in summer to winter but not in spring (p<0.05; n=42). An application of Self-Organizing Map (SOM) model to the database returned three clusters, and strong seasonality in water quality as well as hydrological characteristics was identified. Long-term rainfall data exhibited no statistical tendencies of increase or decrease, with respect to total annual rainfall. In comparison, summer rainfall increased while that of winter (December to the following February) decreased. Summer rainfall affected total dam discharges in all seasons in a positive manner (p<0.05; n=14), corresponding to winter water quality of the lower Nakdong River likely to be dependent largely upon changes in summer rainfall. Therefore, the development of an effective management strategy for summer rainfall and dam discharge control is strongly required.

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“…There are many handcrafted features: HoC, Scale-Invariant Feature Transform (SIFT), HOG, Local Binary Patterns (LBP), and so on, that can be produced from an image part. The proposed algorithm chooses HoC [7] and HoG [13] as the candidates for the local dominant feature for their successfulness in visual tracking in the past decades.…”

Section: Dominant Feature For Trackingmentioning

confidence: 99%

“…As complementation of the appearance, the spatial structural constraint plays an important role in the proposed scheme. For example, there are false targets that are similar to the groundtruth target in the distribution of the appearance, such as Color Histogram (HoC) [7] or Histogram of Gradient Orientations (HoG) [13]. But they are different from the ground-truth target in the spatial arrangements of image patches.…”

Section: Introductionmentioning

confidence: 99%

Occlusion-aware Visual Tracker using Spatial Structural Information and Dominant Features

2021

IAJIT

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To overcome the problem of occlusion in visual tracking, this paper proposes an occlusion-aware tracking algorithm. The proposed algorithm divides the object into discrete image patches according to the pixel distribution of the object by means of clustering. To avoid the drifting of the tracker to false targets, the proposed algorithm extracts the dominant features, such as color histogram or histogram of oriented gradient orientation, from these image patches, and uses them as cues for tracking. To enhance the robustness of the tracker, the proposed algorithm employs an implicit spatial structure between these patches as another cue for tracking; Afterwards, the proposed algorithm incorporates these components into the particle filter framework, which results in a robust and precise tracker. Experimental results on color image sequences with different resolutions show that the proposed tracker outperforms the comparison algorithms on handling occlusion in visual tracking

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Distance Measures in the Training Phase of Self-Organizing Map for Color Histogram Generation in Spectral Image Retrieval

Cited by 6 publications

References 17 publications

Deciphering the signaling mechanisms of the plant cell wall degradation machinery in Aspergillus oryzae

Deciphering the signaling mechanisms of the plant cell wall degradation machinery in Aspergillus oryzae

Impact of summer rainfall on the seasonal water quality variation (chlorophyll a) in the regulated Nakdong River

Occlusion-aware Visual Tracker using Spatial Structural Information and Dominant Features

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