Theobjective ofthis study istodevelop and robust tochanges ofthebackground. Texture isaproperty of examine theperformance ofanimage classification system using virtually anysurface, including trees, walls, bricks, hair etc. It FuzzyC-Means (FCM)onalarge setofimages represented by contains important structural information ofsurfaces andwell MPEG-7low-level descriptors. Thisexperimental data setconsistsastheir relationship tothesurrounding environment. offive different categories ofimages. Inaseries ofexperiments we considered 5different categories oftheMPEG-7descriptors related ItISworthstressing thatthequality ofacontent-based tocolors andtextures ofimages. Prior toanyclustering the imageretrleval system strongly depends onthecholce ofthe original spacewasreduced usingthestandard Principal setof low-level visualfeatures. For example,the Component Analysis (PCA). A series ofcarefully organized indoor/outdoor classification (7)canbewellperformed using experiments hasledtoanumber ofinteresting findings astothe global colorhistograms andlocal colordescriptors. On the suitability offuzzy sets intheframework inimage organization and other hand,edgehistograms appear tobeuseful incaseof description, insights into thestructure ofvarious categories and city/landscape classification (8).Forinstance, cityimages their interrelationship. usually contain horizontal andvertical edges. Ingeneral, thedimensionality oftheproblem (viz. the I. INTRODUCTION original feature space) isexcessively high andthis calls forthe Givenanomnipresence ofvisual information, image useofsomereduction techniques. Therearesomeimportant understanding becomes oneofthemostevident challenges. advantages behindspacereduction. At first, clustering Considering theexistence ofvarious archives (say, art algorithms usedinthereduced spaces aremoreeffective In galleries, photograph archives, personalized digital albums,thisstudy weconfine ourselves totheprincipal component etc.) thereisa growingdemandforadvanced query analysis (PCA)(9)regarded asvehicle fordimensionality mechanisms that arecapable ofaddressing perceptual aspectsreduction. ofvisual information. Inorder toaddress theneedsof the Thepaper isarranged inthefollowing manner. Section II users, anumberofcontent-based imageretrieval techniques outlines themethodology usedinthestudy. Section III havebeendeveloped,cf.
As big data often contains a significant amount of uncertain, unstructured, and imprecise data that are structurally complex and incomplete, traditional attribute reduction methods are less effective when applied to large-scale incomplete information systems to extract knowledge. Multigranular computing provides a powerful tool for use in big data analysis conducted at different levels of information granularity. In this article, we present a novel multigranulation supertrust fuzzy-rough set-based attribute reduction (MSFAR) algorithm to support the formation of hierarchies of information granules of higher types and higher orders, which addresses newly emerging data mining problems in big data analysis. First, a multigranulation supertrust model based on the valued tolerance relation is constructed to identify the fuzzy similarity of the changing knowledge granularity with multimodality attributes. Second, an ensemble consensus compensatory scheme was adopted to calculate the multigranular trust degree based on the reputation at different granularities to create reasonable subproblems with different granulation levels. Third, an equilibrium method of multigranular coevolution is employed to ensure a wide range of balancing of exploration and exploitation, and this strategy can classify super elitists' preferences and detect noncooperative behaviors with a global convergence ability and high search accuracy. The experimental results demonstrate that the MSFAR algorithm achieves a high performance in addressing uncertain and fuzzy attribute reduction problems with a large number of multigranularity variables.
Bagging has achieved great success in the field of machine learning by integrating multiple base classifiers to build a single strong classifier to reduce model variance. The performance improvement of bagging mainly relies on the number and diversity of base classifiers. However, traditional deep learning model training methods are expensive to train individually and difficult to train multiple models with low similarity in a restricted dataset. Recently, diffusion models, which have been tremendously successful in the fields of imaging and vision, have been found to be effective in generating neural network model weights and biases with diversity. We creatively propose a Bagging deep learning training algorithm based on Efficient Neural network Diffusion (BEND). The originality of BEND comes from the first use of a neural network diffusion model to efficiently build base classifiers for bagging. Our approach is simple but effective, first using multiple trained model weights and biases as inputs to train autoencoder and latent diffusion model to realize a diffusion model from noise to valid neural network parameters. Subsequently, we generate several base classifiers using the trained diffusion model. Finally, we integrate these ba se classifiers for various inference tasks using the Bagging method. Resulting experiments on multiple models and datasets show that our proposed BEND algorithm can consistently outperform the mean and median accuracies of both the original trained model and the diffused model. At the same time, new models diffused using the diffusion model have higher diversity and lower cost than multiple models trained using traditional methods. The BEND approach successfully introduces diffusion models into the new deep learning training domain and provides a new paradigm for future deep learning training and inference.
