A Detection Network United Local Feature Points and Components for Fine-Grained Image Classification

Abstract

AbstractIn recent years, fine-grained image classification has been a new research field in computer vision due to the characteristics of significant intra-class differences and minor inter-class differences in fine-grained image classification tasks. Traditional image classification algorithms are still challenging to obtain good classification results despite relying on manual annotation. Since minor local differences can only distinguish the subcategories, accurate detection of local details is the key to improving fine-grained classification accuracy. Therefore, this paper proposes a joint detection network model of local feature points and components for fine-grained image classification to effectively predict and extract local feature positions. Experiments verify the effectiveness of the proposed method on the public data set CalTech-UCSD Birds (CUB 200-2011) for fine-granularity classification tasks.KeywordsLandmark and parts detectionSpatial transformer networksJoint detection modelBase model modificationImage fine-grained classification