Image collection scene sorting method and image collection scene sorting device based on spectrogram analysis

Abstract

The invention discloses an image collection scene sorting method and an image collection scene sorting device based on spectrogram analysis. The method comprises the following steps of extracting scale invariant feature transform (SIFT) characteristic collection of image collection to obtain K clusters and K codons; building an entitled spectrogram G = (V, E) according to SIFT characteristics and K codons of any image; confirming K nodes with the least European distance with any node; obtaining a weight matrix W corresponding to node collection V; achieving divergence operator diagonal matrix D according to W; obtaining laplace operator matrix L according to W and D; computing the L to obtain interaction time between each SIFT characteristic and K codons of any image, and confirming membership degree of interaction time; and confirming distribution results of the codons according to the membership degree, and sorting scene according to distribution results. The method is accurate in confirming membership degree by interaction time, prevents nonlinear data from being lost, and further improves accuracy of sorting results. The device is simple in structure and easy to produce.

Description

technical field [0001] The present invention relates to the technical field of computer vision, in particular to a method and device for classifying scenes based on image collections analyzed by spectrograms. Background technique [0002] The goal of scene classification is to obtain the macro-semantic information of the input image that conforms to the general cognition of human beings, which is an important component of image understanding technology in computer vision. It needs to establish the connection between the underlying image visual features (such as color, edge, texture, etc.) and the high-level scene semantic concept, and obtain some basic scene category information (such as forest, city, coast, sky, indoor, etc.). With the continuous development of digital image acquisition technology, the content of image databases is getting larger and larger, and traditional manual labeling becomes more and more difficult. Therefore, using computers to automatically label se...

AI Technical Summary

Problems solved by technology

The disadvantage of the existing feature bag-based scene classification method is that in codeword assignment, only the Euclidean distance between each SIFT feature and codeword is used to determine the degree of membership between them

And they are all distributed in high-dimensional (128-dimensional) data, directly using Euclidean distance will lose a lot of nonlinear information, thereby reducing the effect of classification

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