ABSTRACT
Population ageing is an important global issue. The Taiwanese government has used various Internet of Things (IoT) applications in the “10-year long-term care program 2.0”. It is expected that the efficiency and effectiveness of long-term care services will be improved through IoT support. Home-delivered meal services for the elderly are important for home-based long-term care services. To ensure that the right meals are delivered to the right recipient at the right time, the runners need to take a picture of the meal recipient when the meal is delivered. This study uses the IoT-based image recognition system to design an integrated service to improve the management of image recognition.
The core technology of this IoT-based image recognition system is statistical histogram-based k-means clustering for image segmentation. However, this method is time-consuming. Therefore, we proposed using the statistical histogram to obtain a probability density function of pixels of a figure and segmenting these with weighting for the same intensity. This aims to increase the computational performance and achieve the same results as k-means clustering. We combined histogram and k-means clustering in order to overcome the high computational cost for k-means clustering. The results indicate that the proposed method is significantly faster than k-means clustering by more than 10 times.
MATERIALS AND METHODS
For the convenience of evaluating the segmentation results and the computational performance, our research images were captured from UC Berkeley EECS’s specialized segmentation dataset on an online address. The images were copyright-free. Due to the abundance, we selected three categories from the segmentation dataset, which were figures, landscapes, and buildings. There were two images selected for each category, resulting in a total of six images for evaluation. In addition, the image pixel size has two types of specifications, which were 321 × 481 or 481 × 321.
EXPERIMENTAL RESULTS AND DISCUSSION
For the experiment, we chose the major constituent areas as the images of personages, landscapes, and buildings. Following this, we discussed the experimental segmentation results and operation speeds of binary processing, quad processing, hexad processing, and octad processing images. We chose two images from each category for the experiment, with each image evaluated 10 times. After this, we chose the two most representative images (they are respectively in Tables 1–3) for discussion and comparison.
CONCLUSIONS
This study proposes a method that can significantly improve the deficits of the original KMC method. Furthermore, the image experiments prove that there is no significant difference between the KMC method and the KMC method based on the statistical histogram when using binary, quad, hexad, and octad processing. At the same time, the obvious enhancement of computing speed is supported in the experiment.
In the segmentation by binary processing, the speed of the k-means clustering method based on the statistical histogram is 11 times faster. This speed is 22 times faster in the segmentation by quad processing; 37 times faster in the segmentation by hexad processing; and 54 times faster in the segmentation by octad processing. However, HKMC only increases the time by 88%, while the original KMC increases the computing time by 842%.
Therefore, the speed of the k-means clustering method based on the statistical histogram proposed in this article is indeed faster than that of the original KMC method, with no subsequent influences on the segmentation results. Therefore, the HKMC method can be applied more readily to multi-valued segmentations. Finally, this study suggests that future plans can be combined with more IoT-based life applications, such as Unmanned Aerial Vehicle (UAV) home delivery, parcel delivery, and other intelligent application designs to promote image recognition technology.
Source: National Chiao Tung University,
Authors: Hsiao-ting Tseng | Hsin-ginn Hwang | Wei-yen Hsu | Pei-chin Chou | I-chiu Chang