Abstract
Image self-coupling and feature interference lead to poor retrieval performance in massive image retrieval of mobile terminals. This paper proposes a massive image retrieval method of mobile terminals based on weighted aggregation depth features. The pixel big data detection model of massive images of mobile terminals is constructed, the collected pixel information of massive images of mobile terminals is restructured, the edge contour feature parameter set of massive images of mobile terminals is extracted, the feature fusion processing of massive images of mobile terminals is carried out in gradient pixel space by means of feature reconstruction and gray moment invariant feature analysis, the depth feature detection of massive images of mobile terminals is realized by using weighted aggregation method, the gradient value of pixels of massive images of each mobile terminal is calculated, and the optimized retrieval of massive images of mobile terminals is realized according to the fusion result of gradient weighted information. Simulation results show that this method has better feature clustering, stronger image detection and recognition, and anti-interference ability and improves the precision and recall of image retrieval.
Highlights
With the increasing amount of multimedia image information in the database of mobile terminal, it leads to the increasing difficulty of accurate image retrieval and positioning
The methods for mass image retrieval of mobile terminals mainly include the mass image retrieval method of mobile terminals based on semantic ontology information fusion, the mass image retrieval method of mobile terminals based on cloud computing fusion, and the optimization based on particle swarm PSO and genetic evolution clustering, mass image retrieval methods for mobile terminals, etc
The model of mobile terminal mass image retrieval based on similarity feature detection and semantic ontology fusion is proposed in reference [4], and the parameter model of feature distribution of mobile terminal mass images is constructed
Summary
With the increasing amount of multimedia image information in the database of mobile terminal, it leads to the increasing difficulty of accurate image retrieval and positioning. It is necessary to combine multidistribution feature detection and information fusion of images to build an optimized mass image retrieval algorithm for mobile terminals. Combining the pixel feature analysis of the massive image of the mobile terminal, through the semantic ontology information segmentation and corner location detection of the image, the feature clustering algorithm and the adaptive learning algorithm [2] are used to retrieve the massive image of the mobile terminal. The model of mobile terminal mass image retrieval based on similarity feature detection and semantic ontology fusion is proposed in reference [4], and the parameter model of feature distribution of mobile terminal mass images is constructed. Simulation test analysis is conducted to demonstrate the superior performance of this paper’s method in improving the massive image retrieval capability of mobile terminals
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