Method for reordering image or video search

Abstract

The invention discloses a method for reordering image or video search, which relates to the field of multimedia-oriented information retrieval. The method comprises the following steps of grading an image sample set into a grade A, a grade B and a grade C according to the degrees of inquiry topic relevance; constructing a relevance diagram, an irrelevance diagram and a global diagram; acquiring a relevance divergence, an irrelevance divergence and a global divergence; constructing a target function according to the relevance divergence, the irrelevance divergence and the global divergence, and acquiring a novel characteristic vector of an image sample; inputting the novel characteristic vector of a marked image sample serving as a training set into a training model to obtain a trained ordering model; and ordering the image sample through the trained ordering model and outputting an ordering result. The invention discloses a dimensionality reduction method which belongs to the field relevant to multimedia retrieval and ordering. According to the method, specific properties of data are utilized fully on the premise of limited monitoring information, the ordering performance can be improved by effectively utilizing a small number of marks, and searching precision is increased.

Description

technical field [0001] The invention relates to the field of multimedia information retrieval, in particular to a method for image or video search reordering. Background technique [0002] With the rapid development of information technology, a large number of multimedia data such as images and videos have emerged, which has become one of the important ways for people to obtain information. How to quickly and accurately obtain the information required by users from massive amounts of data is a challenging task. Image or video search re-ranking is the process of re-ranking the retrieval results by using the new ranking model to train the ranking model based on the initial text-based search results combined with other available auxiliary information. User experience and satisfaction. [0003] There is a large amount of sorting information in the data related to multimedia retrieval. Sorting information refers to the supervisory information provided by the training data set ...

AI Technical Summary

Problems solved by technology

[0007] The image or video search reordering methods in the prior art usually do not perform dimensionality reduction processing on the extracted high-dimensional feature vectors or perform unsupervised dimensionality reduction, or simply store the correlation level information of images or videos Dimensionality reduction is performed as category label information. However, since these data usually have high-dimensional characteristics, directly analyzing and processing them will lead to the following important problems: 1) High computational complexity; 2) High storage cost; 3) Dimensional number of disasters

This has become a key issue that seriously restricts the field of multimedia content analysis and retrieval

In addition, in image or video search reordering, the category labels of images or videos cannot fully and accurately describe the relationship between images or between videos, so the correlation level information of images or videos is directly used as a category Label information reduces the dimensionality of the data, making the trained sorting model inaccurate and the retrieval accuracy low, which cannot meet the needs of practical applications

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