Multiple-Instance Learning Image Database Retrieval employing Orthogonal Fractal Bases

• Main Authors: Ya-ling Wang, 王雅羚
• Other Authors: none
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/82360665752280931848

碩士 === 國立中山大學 === 資訊工程學系研究所 === 92 === The objective of the present work is to propose a novel method to extract a stable feature set representative of image content. Each image is represented by a linear combination of fractal orthonormal basis vectors. The mapping coefficients of an image projected onto each orthonormal basis constitute the feature vector. The set of orthonormal basis vectors are generated by utilizing fractal iterative function through target and domain blocks mapping. The distance measure remains consistent, i.e., isometric embedded, between any image pairs before and after the projection onto orthonormal axes. Not only similar images generate points close to each other in the feature space, but also dissimilar ones produce feature points far apart. The above statements are logically equivalent to that distant feature points are guaranteed to map to images with dissimilar contents, while close feature points correspond to similar images. In this paper, we adapt the Multiple Instance Learning paradigm using the Diverse Density algorithm as a way of modeling the ambiguity in images in order to learning concepts used to classify images. A user labels an image as positive if the image contains the concepts, as negative if the image far from the concepts. Each example image is a bag of blocks where only the bag is labeled. The User selects positive and negative image examples to train the concepts in feature space. From a small collection of positive and negative examples, the system learns the concepts using them to retrieve images that contain the concepts from database. Each concept having similar blocks becomes the group in each image. According groups’ location distribution, variation and spatial relations computes positive examples and database images similarity.