IEEE Trans Image Process - Local energy pattern for texture classification using self-adaptive quantization thresholds.

Tópicos

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{ learn(2355) train(1041) set(1003) }
{ data(3963) clinic(1234) research(1004) }
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Resumo

Local energy pattern, a statistical histogram-based representation, is proposed for texture classification. First, we use normalized local-oriented energies to generate local feature vectors, which describe the local structures distinctively and are less sensitive to imaging conditions. Then, each local feature vector is quantized by self-adaptive quantization thresholds determined in the learning stage using histogram specification, and the quantized local feature vector is transformed to a number by N-nary coding, which helps to preserve more structure information during vector quantization. Finally, the frequency histogram is used as the representation feature. The performance is benchmarked by material categorization on KTH-TIPS and KTH-TIPS2-a databases. Our method is compared with typical statistical approaches, such as basic image features, local binary pattern (LBP), local ternary pattern, completed LBP, Weber local descriptor, and VZ algorithms (VZ-MR8 and VZ-Joint). The results show that our method is superior to other methods on the KTH-TIPS2-a database, and achieving competitive performance on the KTH-TIPS database. Furthermore, we extend the representation from static image to dynamic texture, and achieve favorable recognition results on the University of California at Los Angeles (UCLA) dynamic texture database.

Resumo Limpo

local energi pattern statist histogrambas represent propos textur classif first use normal localori energi generat local featur vector describ local structur distinct less sensit imag condit local featur vector quantiz selfadapt quantiz threshold determin learn stage use histogram specif quantiz local featur vector transform number nnari code help preserv structur inform vector quantiz final frequenc histogram use represent featur perform benchmark materi categor kthtip kthtipsa databas method compar typic statist approach basic imag featur local binari pattern lbp local ternari pattern complet lbp weber local descriptor vz algorithm vzmr vzjoint result show method superior method kthtipsa databas achiev competit perform kthtip databas furthermor extend represent static imag dynam textur achiev favor recognit result univers california los angel ucla dynam textur databas

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