Image retrieval is to find out the similar semantic images to the query image, which is an important task in the field of image recognition. It is still an open challenging task due to the semantic gap of image understanding. The traditional image retrieval method is a simple retrieval between the query image and the database. However, only a query image contains weaker category information, so that the traditional image-based retrieval results are not satisfactory. In this paper, we propose a category pattern mining (CPM) strategy to extend an image (point) to an image category (plane). It means the semantic extension is performed from the individual query image to the whole image category. The proposed PTP (point to plane) method mined the category pattern of the query image and enriched the semantic information. The main contribution of the PTP framework is to improve the image retrieval from the traditional image-based retrieval into the new category-based retrieval. Experimental results and evaluations on two databases demonstrate that the proposed PTP method achieves an obvious superiority in the image retrieval tasks.
Trajectory tracking technology is one of the key technologies of unmanned vehicles, and accurate trajectory tracking control is also the primary premise for unmanned vehicles to complete various military tasks. At present, the trajectory tracking research of unmanned vehicle mostly relies on the research results of mobile robot. The traditional trajectory tracking control methods mainly include PID control, neural network control, optimal control, sliding mode control and predictive control. They have their own advantages and limitations. By studying the control behavior of vehicle drivers, using intelligent control method, this paper establishes a nonlinear time-varying dynamic model suitable for unmanned vehicles, breaks through the key technologies such as accurate trajectory tracking control of unmanned vehicles, and provides technical support for the development of new unmanned vehicles in practical environment and the unmanned transformation of active vehicles.
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