Neural Network Based Clothing Style Analysis via Deep Filter Bank
| Autor: | Chen-Kuo Chiang, Chia-Wei Tu |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Artificial neural network
Time delay neural network Computer science business.industry Feature extraction Pattern recognition 02 engineering and technology 010501 environmental sciences Filter bank Clothing 01 natural sciences Convolutional neural network Style analysis 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | 2016 International Computer Symposium (ICS). |
| DOI: | 10.1109/ics.2016.0076 |
| Popis: | A neural network based clothing style analysis method is proposed via deep filter bank in this paper. Clothing styles are complicated and high-level concept. We propose to construct the deep filter bank by combining Convolution Neural Network(CNN) with Fisher Vector(FV). Then, the extracted features from the body part are used to train the Part-CNN (p-CNN) model. Multiple p-CNNs are integrated along with the whole body model to train the Clothing Style Analysis Neural Network (CSANN). Experimental results on several benchmark datasets show that the superior performance of the proposed method. |
| Databáze: | OpenAIRE |
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