Clothing Brand Logo Prediction: From Residual Block to Dense Block

Kuan-Hsien Liu, Tsung-Jung Liu, Fei Wang

Abstract: In this paper, we proposed a new clothing brand prediction method which is rooted on a dense-block based deep convolutional neural network for brand logo detection and recognition. To learn convolutional neural networks deeper and more accurately, we adopted dense blocks into deep convolutional neural networks to make connections between layers shorter. In this work, we propose several dense-block based designs to improve clothing brand logo detection and recognition accuracies. We also constructed a new large-scale clothing brand and price (CBP) dataset and its subset, called clothing brand logo (CBL) dataset with the brand attribute and logo information to carry out this task. To lower proposed framework complexity, two pixel search steps for the bounding box movement are implemented in the training procedure. In the experiment, we show our search reduced model can outperform several state-of-the-art methods and attain good performance.