Large-Scale Image Retrieval of Tourist Attractions Based on Multiple Linear Regression Equations

Abstract

This paper presents an in-depth study and analysis of large-scale tourist attraction image retrieval using multiple linear regression equation approaches. This feature extraction method often relies on the partitioning of the grid and is only effective when the overall similarity of different images is high. The BOF model is borrowed from the method for text retrieval, which generally extracts the local features of an image by the scale-invariant feature transform algorithm and clusters them using k -means to obtain a low-dimensional visual dictionary and characterizes the image features with a histogram vector based on the visual dictionary. However, when there are many kinds of images, the dimensionality of the visual dictionary will be large and it is not convenient to construct the BOF model. The last fully connected layer is taken as the image feature, and it is dimensionalized by the principal component analysis method, and then, the low-dimensional feature index structure is constructed using the locality-sensitive hashing- (LSH-) based approximate nearest neighbor algorithm. The accuracy of our graph retrieval has increased by 8%. The advantages of feature extraction by a convolutional neural network and the high efficiency of a hash index structure in retrieval are used to solve the shortcomings of traditional methods in terms of accuracy and other aspects in image retrieval. The results show that compared with the above two algorithms, for most of the attractions, the method has a relatively obvious advantage in the accuracy of retrieval, and when there are few similar images of a particular attraction in the attraction image library, the accuracy of the query results is not much different from the first two methods.

Study on Influencing Factors of Tourist Flow in Scenic Spots Based on Big Data

“…CNNs can be improved to increase their accuracy and decrease their failure rate. Building a more complete and accurate database of tourist attractions' images can improve image retrieval performance while simultaneously decreasing the failure rate of information retrieval, which can be achieved by constantly enhancing feature extraction algorithms and index processes to enhance the image processing techniques of huge tourist destinations [17]. Tourism demand forecasting relies heavily on the research of factors that in uence demand.…”