Research On Fine-grained Image Recognition Algorithm Based On Weakly Supervised Learning

In recent years,fine-grained image recognition has been widely concerned in the field of computer vision.Its task is to identify the object subclasses that are very similar in appearance information and semantic information,such as identifying different kinds of "birds",such as "Brown headed Brucea","hundred ear thrush","red bellied tit"(or identifying different kinds of cars,such as "Toyota","Honda","Lu Tiger " has a wide application prospect.Compared with the traditional image classification,the differences and difficulties of fine-grained image recognition are as follows: first,there are very small differences between classes(such as different subclasses of birds or different subclasses of cars),and the differences between different subclass objects are mainly reflected in the local details;second,for fine-grained image,there are large intra class differences,that is,the same subclass image itself has shape,posture,color,background,etc.Therefore,how to detect the object parts with resolution and how to extract the fine-grained features better has become an urgent problem in the field of fine-grained recognition.This paper mainly studies fine-grained image recognition algorithm based on deep learning.According to whether the label information other than image classification(such as boundary box annotation of object parts)is used in training stage,fine-grained image classification algorithms can be divided into two categories: fine-grained image recognition based on strong supervised learning and fine-grained image recognition based on weak supervised learning.In this paper,two algorithms are proposed to use weak supervised learning to make the network learn to locate the resolution region in the object and extract the finegrained features.The main contents of this paper are as follows:(1)A fine-grained recognition algorithm combining non-local and multi-area attention mechanism is proposed.Through the introduction of navigation module to achieve the weak surveillance location of the discriminative region.On this basis,for the navigation module does not consider the relationship between different locations,the non local module and the navigation module are introduced to enhance the global information perception ability of the model.Then,the feature extraction network based on channel attention mechanism is constructed to strengthen the connection between feature channels,which makes the network pay more attention to the more important feature channels.Experimental results on three open fine-grained image recognition libraries,CUB-200-2011,Stanford Cars and FGVC Aircraft,show that the model combined with non-local module and channel attention mechanism has a significant improvement in accuracy compared with the model using only navigation module,and is higher than a variety of comparison algorithms.(2)A fine-grained image recognition algorithm is proposed,which combines multi-level cross bilinear pooling and visual attention mechanism.In this algorithm,Res Net-50 is used as the reference network,and two multi-level cross bilinear modules of different scales are constructed on Res Net-50.The output characteristics of these two multi-level cross bilinear modules and the last global mean pooled output characteristics are used as the final feature representation.Secondly,a training mechanism of "region destruction" is introduced,that is,in addition to the original image,the original image after "destruction" is also input during the training,and an adversary loss is added to solve the noise introduced after image destruction.In this way,if the model wants to recognize an image correctly,it must "be forced" to pay attention to the object’s resolution area and detail features.This training method makes the model have "visual attention".The experimental results on three open fine-grained image databases show that the recognition accuracy of the proposed algorithm is higher than that of many comparison algorithms.