Research On Multi-object Recognition Based On Probabilistic Graph Model

In recent years, digital image object recognition has been applied in various as-pects of life, touching areas such as camera face detection, illegal road vehicle licenseplate recognition, image indexing and intelligent robot vision. Early object recognitionresearch mainly focused on the extraction of local features like color, texture, edge andcorner, which lacks the sense of global understanding. Recent research focus has putmore emphasize on global features such as contour and positions in order to improveobject detection precision. In this paper, we focus on extracting both local and globalfeatures and establish a multi-object classi?cation model by integrating these features.Based on the Markov Random Field theory, we establish a multi-object recogni-tion model. This model encodes local features and global position features. For localfeatures, we extract information from color, texture, edge and corner to form a 17 di-mensional feature vector. For global features, this paper ?rst proposes an angle featureextraction method that uses angle feature to describe various parts of a single object inthespatialdistributionofmutualrelations. Then, byextendingthismethod, wecapturethe spatial layout information of di?erent objects within an image.The Markov Random Field based multi-class recognition system is established bythe following four steps: segmenting image into superpixels,extracting image'localvisual features and training the basic classi?er based on these local features, extractingthe global spacial layout features from images, and training the synthesized multi-classrecognition model. The learnt model can be used to classify the objects in the testimages.Weevaluateourmodelontwodifferentimagedatabases. TheresultsshowthattheMarkovrandomfieldincorporatingspaciallayoutinformationallowsustosigni?cantly increase the classification accuracy compared with the models based on local features.