In this paper, we propose a novel personalized ranking system for amateur photographs. Although some of the features used in our system are similar to previous work, new features, such as texture, RGB color, portrait (through face detection), and black-and-white, are included for individual preferences. Our goal of automatically ranking photographs is not intended for award-wining professional photographs but for photographs taken by amateurs, especially when individual preference is taken into account. The performance of our system in terms of precision-recall diagram and binary classification accuracy (93%) is close to the best results to date for both overall system and individual features. Two personalized ranking user interfaces are provided: one is feature-based and the other is example-based. Although both interfaces are effective in providing personalized preferences, our user study showed that example-based was preferred by twice as many people as feature-based.
Hand gesture recognition (HGR) is one of the widely-used human-computer interaction technology. With HGR, the user can operate the interaction system without touching any devices. For a better experience, recognition accuracy and computational speed are especially important. In this work, a small-footprint HGR model and its hardware architecture design are proposed. The model first processes the hand segmentation and uses the feature to recognize the hand gesture. The model mainly consists of depthwise separable convolution to reduce the overall parameters and computations. We transfer the hand segmentation task with some features to the hand gesture recognition task as a single-stage model. Based on this hardware-efficient model, we propose the hardware architecture of the whole neural model including depthwise convolution, pointwise convolution, batch normalization, and max-pooling. We also demonstrate it on the evaluation board. The whole system is implemented on the Xilinx ZCU106 evaluation board. The implemented system can achieve the performance of 52.6 fps and 65.6 GOPS based on the evaluation board.INDEX TERMS Hand gesture recognition, attention model, depthwise separable convolution, hardware accelerator, field-programmable gate array (FPGA).
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