Aiming at shortcomings of traditional image retrieval systems, a new image retrieval approach based on color features of image combining intuitive fuzzy theory with genetic algorithm is proposed. Each image is segment...Aiming at shortcomings of traditional image retrieval systems, a new image retrieval approach based on color features of image combining intuitive fuzzy theory with genetic algorithm is proposed. Each image is segmented into a constant number of sub-images in vertical direction. Color features are extracted from every sub-image to get chromosome coding. It is considered that fuzzy membership and intuitive fuzzy hesitancy degree of every pixel＇s color in image are associated to all the color histogram bins. Certain feature, fuzzy feature and intuitive fuzzy feature of colors in an image, are used together to describe the content of image. Efficient combinations of sub-image are selected according to operation of selecting, crossing and variation. Retrieval results are obtained from image matching based on these color feature combinations of sub-images. Tests show that this approach can improve the accuracy of image retrieval in the case of not decreasing the speed of image retrieval. Its mean precision is above 80 %.展开更多
Aiming at the problem of video key frame extraction, a density peak clustering algorithm is proposed, which uses the HSV histogram to transform high-dimensional abstract video image data into quantifiable low-dimensio...Aiming at the problem of video key frame extraction, a density peak clustering algorithm is proposed, which uses the HSV histogram to transform high-dimensional abstract video image data into quantifiable low-dimensional data, and reduces the computational complexity while capturing image features. On this basis, the density peak clustering algorithm is used to cluster these low-dimensional data and find the cluster centers. Combining the clustering results, the final key frames are obtained. A large number of key frame extraction experiments for different types of videos show that the algorithm can extract different number of key frames by combining video content, overcome the shortcoming of traditional key frame extraction algorithm which can only extract a fixed number of key frames, and the extracted key frames can represent the main content of video accurately.展开更多
基金Sponsored by the Ministerial Level Foundation(20061823)
文摘Aiming at shortcomings of traditional image retrieval systems, a new image retrieval approach based on color features of image combining intuitive fuzzy theory with genetic algorithm is proposed. Each image is segmented into a constant number of sub-images in vertical direction. Color features are extracted from every sub-image to get chromosome coding. It is considered that fuzzy membership and intuitive fuzzy hesitancy degree of every pixel＇s color in image are associated to all the color histogram bins. Certain feature, fuzzy feature and intuitive fuzzy feature of colors in an image, are used together to describe the content of image. Efficient combinations of sub-image are selected according to operation of selecting, crossing and variation. Retrieval results are obtained from image matching based on these color feature combinations of sub-images. Tests show that this approach can improve the accuracy of image retrieval in the case of not decreasing the speed of image retrieval. Its mean precision is above 80 %.
文摘Aiming at the problem of video key frame extraction, a density peak clustering algorithm is proposed, which uses the HSV histogram to transform high-dimensional abstract video image data into quantifiable low-dimensional data, and reduces the computational complexity while capturing image features. On this basis, the density peak clustering algorithm is used to cluster these low-dimensional data and find the cluster centers. Combining the clustering results, the final key frames are obtained. A large number of key frame extraction experiments for different types of videos show that the algorithm can extract different number of key frames by combining video content, overcome the shortcoming of traditional key frame extraction algorithm which can only extract a fixed number of key frames, and the extracted key frames can represent the main content of video accurately.
