Visual word bag model constructing model based on improved SURF characteristic

Abstract

The invention provides a visual word bag model constructing model based on an improved SURF characteristic. A box filtering template added with gradient information is used to replace a Gaussian filter, and the template is more close to a Gaussian second-order differential template. In SURF characteristic expression, the overhead in time is reduced, and a SURF descriptor reduced to 32 dimensions while rotation invariance is ensured. When a word bag is constructed, an improved SURF algorithm is used to extract all improved SURF characteristic in an image library, a k-means clustering method is used to cluster all SURF characteristics as a visual word, and each image is expressed as the high-dimensional vector of the appearance frequency of each visual word. The comprises gradient information with rich images, one time of calculating a Haar wavelet is omitted, compared with the direct use of the SURF characteristic, the problem of non-uniform characteristic amounts extracted from different images can be solved well, the a word bag function allows multiple images to be expressed by a certain number of visual words, the space is saved, the processing is convenient, and the scalability is high.

Description

technical field [0001] The invention relates to a method for constructing a visual bag-of-words model based on improved SURF features, and belongs to the technical field of computer vision. Background technique [0002] Compared with the global features of the image, the local features of the image can better describe the image in the face of complex background, large noise interference, changing lighting conditions, multiple things and complex semantics. In recent years, it has been widely used in Image registration, recognition, retrieval, classification and other fields. When directly using local features for image classification and image retrieval, since the number of feature points detected by each image in the image database is not uniform, and commonly used local features such as SIFT, SURF, and DAISY features are high-dimensional features, each image Images are all represented by varying numbers of high-dimensional features, resulting in low efficiency when computi...

AI Technical Summary

Problems solved by technology

The SIFT feature is a highly robust feature extracted based on some local appearance points of interest on the object. It has nothing to do with the size and rotation of the image. It has a high tolerance for light, noise, and micro-angle changes, but the calculation is also relatively complicated. , the efficiency is relatively low

Many scholars have improved the SIFT algorithm. SURF is an improved algorithm of SIFT. The use of integral images and box filters in SURF greatly improves the efficiency of the algorithm, and the processing speed is about three times higher than that of SIFT. However, the SURF algorithm still has certain limitations. Disadvantages: Replacing Gaussian filtering with integral image and box filtering, although the speed is improved, but the gradient information in the image is unavoidably lost; when SURF descriptors are generated, it is necessary to calculate the Haar wavelet response of the circular neighborhood first to obtain the feature points. In the main direction, the Haar wavelet response of the square neighborhood is calculated again to obtain a 64-dimensional vector. This repeated calculation process has a certain impact on the efficiency of SURF

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