Image Attribute Clustering Method Based on Group Genetic Algorithm

Abstract

The present invention provides an image attribute clustering method based on a group genetic algorithm, comprising: a first step: for N images, randomly generating N chromosomes as an initial population, wherein each chromosome represents a possible attribute clustering result, Each image has m attributes, and the attributes of the image are divided into multiple groups; the second step: calculate the fitness function value of each chromosome; the third step: use the gambler to select according to the calculated chromosome fitness function value Strategy selection of next generation N chromosomes; Step 4: Execute the crossover operator on the group part of the chromosome; Step 5: Execute the mutation operator on the attribute part of the chromosome; Step 6: Execute the inversion operator on the attribute part of the chromosome ; The seventh step: repeat the second step to the sixth step until the given genetic algebra is completed; the eighth step: output the chromosome with the optimal fitness function value obtained by completing the given genetic algebra.

Description

technical field [0001] The present invention relates to the field of image recognition, more specifically, the present invention relates to an image attribute clustering method based on group genetic algorithm. Background technique [0002] Learning-based image recognition methods have made great progress in the past few years. For specific object classes, especially faces and cars, there are relatively reliable and efficient recognition based on underlying features (such as SIFT (Scale Invariant Feature Transform) features or HOG (Histogram of Oriented Gradient) features). But the underlying features of these images cannot reflect the image category information well. Recently, new research materials propose methods for classification using intrinsic properties of images. Attributes refer to the characteristics that can be named by people and can be observed in the image. It can indicate whether the object in the image exists, can describe the color, shape, material, compo...

AI Technical Summary

Problems solved by technology

Genetic algorithm is good at global search, but its local search ability is obviously insufficient, the speed of searching for the optimal solution or satisfactory solution is too slow, and there are problems such as easy to fall into the local optimal solution and premature convergence caused by excessive selection pressure.

Moreover, due to operations such as cross-mutation, different chromosomes may represent the same attribute clustering results, which increases the scope of the search and wastes time.

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