Image Retrieval Method Based On Multi-scale Feature Fusion

Image retrieval technology aims to search similar images from large-scale image data sets according to the input images.In short,image retrieval is a process of searching images by images.With the development of computer vision,image processing is more and more widely used in daily life.As an important branch of image processing,image retrieval technology plays an irreplaceable role in remote sensing,military,medicine,e-commerce and other fields.In recent years,image retrieval technology has developed rapidly,various algorithms emerge in endlessly,change and transform,and the retrieval effect is also improving.However,due to the continuous change of real scenes and the increasing complexity of image data sets,there are still some challenges in the retrieval task,such as high feature dimension,large scale change,complex scenes and similarity of targets.In view of the above problems,this thesis carries out analysis and research,and puts forward the improvement scheme with the best retrieval effect according to the shortcomings and limitations of various algorithms.The main innovative contributions are as follows:(1)Aiming at the problem of slow retrieval speed caused by high feature dimension and large scale change in the retrieval process,a multi-scale image retrieval algorithm based on color and texture features is proposed.Firstly,three different multiscale Gaussian spaces are constructed to sample the input image,and the macro and micro principal curvature information of each scale image is extracted by Hessian matrix,which is input into FHOG descriptor to extract the multi-scale texture information of the image;At the same time,the image is converted from RGB color space to HSV color space for quantitative analysis.The color feature histogram of the image is constructed by setting the weight coefficient of the color channel,and the color information of the image is extracted;Finally,the multi-scale texture information and color information of the image are weighted and fused to resist scale change,while the traditional feature dimension extracted is low,which can effectively reduce the retrieval time and improve the real-time performance of the retrieval while ensuring the retrieval effect.(2)Aiming at the problems of complex scenes and similarity of targets in the retrieval process,a multi-scale image retrieval algorithm combining traditional and depth features is proposed.Firstly,the inception-v4 network with sub module and parallel convolution is selected to extract the multi-scale depth features of the image.On this basis,the channel attention mechanism is introduced to improve the feature extraction effect of each channel by adaptively adjusting the weight;Then,we continue to introduce the spatial attention mechanism to locate the target location and improve the retrieval performance of the network on the basis of obtaining the channel information and combining the characteristics of the image spatial domain.Finally,the feature stitching layer is used to effectively fuse the traditional features and depth features of the image to obtain more detailed global features of the image,so as to resist the complex scenes and similar targets in the data set and improve the effect of retrieval.In order to verify the effectiveness of this algorithm,experiments are carried out on Corel-1K,Coil-100 and FVTL.The results show that the algorithm proposed in this thesis can effectively deal with the scale change of the data set and improve the retrieval speed.At the same time,it can further improve the retrieval effect for the complex scenes and similar targets in the data set.