Video high-level characteristic retrieval system and realization thereof

Abstract

The invention provides a video high-level characteristic retrieval system based on a plurality of bottom-level characteristics of color, edge, texture, characteristic point and the like and a supportvector machine (SVM). Shot boundary detection is firstly carried out on a video clip, and then a plurality of representative frames are extracted at equal intervals from a shot to be as key frames. For the extracted key frames, a plurality of robust bottom-level characteristics of color, edge, texture and characteristic point are extracted. The adoption of the bottom-level characteristics provides description in different respects for video high-level semantic characteristics, because the low-level characteristics has strong complementarity and can respectively present strong differentiatingcapability for different semantic concepts, so that the detection performances of the system for different concepts can be ensured. The extracted characteristics are respectively sent to the support vector machine (SVM) for classification to form a multi-branch subsystem. In the concept classification stage, the support vector machine (SVM) is selected as a classifier, a method based on condensednearest neighbor is firstly used for selecting training parameters, so that the ubiquitous problem in training process of imbalance of positive and negative samples is effectively solved. In order tofully utilize the description information provided by a plurality of subsystems, a two-grade integrating strategy is adopted for the classification scores of the multi-branch subsystem, and a method of logistic regression is introduced to learn the optimal integrating strategy, so that the accuracy and the recall ratio of an integrating system are greatly improved.

Description

technical field [0001] The invention belongs to the technical field of video retrieval, and in particular relates to a content-based video lens retrieval method. Its essence is to extract key frame information in the shot, perform multi-category feature extraction on it, and use support vector machine (SVM) to calculate the matching degree of the queried content to form multiple subsystems. The present invention proposes a score fusion method based on Logistic Regression, on the basis of which, a unified high-level video feature retrieval system with high accuracy is obtained. Background technique [0002] The development of multimedia technology and the Internet has brought people a huge ocean of multimedia information, and the rapid rise of IPTV and video websites has further led to the explosive growth of multimedia information such as videos and pictures. The traditional retrieval method based on text keywords has been unable to meet the requirements of multimedia inform...

AI Technical Summary

Problems solved by technology

[0004] In content-based video image retrieval, the main research is the visual and image features of the image, which we call the underlying features, including color, texture, shape, and the spatial relationship formed on this basis. Using visual and image features as an index to retrieve images has the characteristics of simple calculation and stable performance, but these features have certain limitations at present

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