Multi-scale context-guided feature elimination for ancient tower image classification
A multi-scale context-guided feature elimination classification method was proposed,for resolving the problems of ambiguous discriminative feature localization and complex scene interference in the classification task of ancient tower building images.First,a feature extraction network with MogaNet as the core was constructed,and multi-scale feature fusion was combined to fully explore the image information.Next,a context information extractor was designed to utilize the semantic context of the network to align and filter more discriminative local features,enhancing the ability to capture detailed features.Then,a feature elimination strategy was proposed to suppress fuzzy class features and background noise interference,and a loss function was designed to constrain fuzzy feature elimination and classification prediction.At last,a Chinese ancient tower architecture image dataset was established to provide data to support research on complex backgrounds and fuzzy boundaries in the field of fine-grained image categorization.This method achieved 96.3％ accuracy on the self-constructed ancient tower architecture dataset,and 92.4％,95.3％ and 94.6％ accuracy on three fine-grained datasets,namely,CUB-200-2011,Stanford Cars and FGVC-Aircraft,respectively.The proposed method outperforms other comparison algorithms and enables accurate classification of images of ancient tower buildings.
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