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Image Semantic Annotation Based on FSVM

Author: ChangJianFeng
Tutor: FengXiuFang
School: Taiyuan University of Technology
Course: Applied Computer Technology
Keywords: Support Vector Machine Fuzzy Support Vector Machine Semantics Semantic annotation
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 58
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Abstract


In today's society, the image as a carrier of information, due to its inherent large amount of information, and the manifestations of intuitive, more and more attention has been paid, image retrieval techniques to get the attention of scholars at home and abroad has become a hot research topic. Image retrieval technology from the last century, the text-based image retrieval technology began to develop in the 1970s, experienced a content-based image retrieval technology development to the semantic-based image retrieval. Semantic-based image retrieval technology can make up for the defects of the first two retrieval techniques, based on the image of the underlying features of the image with the semantic establish a link to retrieve the corresponding semantics in the semantic space according to demand, and then find the desired image. Semantic-based image retrieval technology, significantly different from the previous two techniques is that the image semantic annotation of images, after retrieval based on semantics, thus this technique, the image semantic annotation becomes an important part of The semantic annotated accuracy affect the final retrieval results. The semantic annotation general mapped by the underlying characteristics of the image directly to semantic during semantic retrieval, image semantic annotation will encounter the problem of the semantic gap, how to solve the problem is the hot spot of the current scholars. Scholars generally use the above method for image semantic annotation usually consider the idea of ??machine learning, using a labeled set of images to train the model can automatically marked image semantics, semantic unlabeled images through the model label. The ideological support vector machine stand out because of its resolve when the advantages of the small sample, study support vector machine theory, improved its the extended fuzzy support vector machine. The theory of fuzzy support vector machine, the fuzzy membership function is an important parameter, which is the probability function of a sample point belongs to a classification used fuzzy membership function in contrast, after a consideration of sample points around sample distribution compact fuzzy membership function. Used fuzzy membership function is generally only considered the relative distance of the center of the sample point to the class, and did not consider the distribution of the sample points around the sample, there are some limitations, consider this compact fuzzy membership function of the sample points around the sample the distribution of the probability of belonging to a certain category, can be more accurately reflected. Introduced the function fuzzy support vector machine, the effect is better than the fuzzy support vector machine effect, the better the accuracy of image annotation.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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