Abstract
Shape matching and object recognition plays an vita l role in the computer vision. The shape matching i s difficult in case of the real world images like mpe g database images since the real world images has t he internal and external contours. The Mahalanobis dis tance based shape context approach is proposed to measure similarity between shapes and exploit it fo r shape retrieval. The process of shape retrieval identifies the relevant shapes from the data base f or the query images. The query image matched with the reference images and it gives the dissimilarity bet ween the shapes. This dissimilarity measures used t o identify the relevant images from the databases. Th e dissimilarity is distance between the two images. The shape matching has the three major steps that a re finding correspondence, measusring distance and the applying allinging transformation. The finding correspondence is find the best matching point between the query image and the reference image, The correspondence is solved by the shape context with Shortest augmenting path algorithm. The measuring distance is used to find the distance between t he corresponding point. In this study, Mehalanobis dis tance is used to find the distance between the imag es. The alligning transformation is used to allign the shapes in order to achieve the best matching point. Object recognition is achieved by the k-nearest nei ghbor algorithm. The proposed method is simple, invariant to noise and gives better error rate comp ared to the existing methods.
Highlights
Owing to the rapid development of digital and information technologies, more and more digital information is generated and available in digital form from varieties of sources around the world
The query image matched with the reference images and it gives the dissimilarity between the shapes
The finding correspondence is find the best matching point between the query image and the reference image, The correspondence is solved by the shape context with Shortest augmenting path algorithm
Summary
Owing to the rapid development of digital and information technologies, more and more digital information is generated and available in digital form from varieties of sources around the world. The feature based method uses the feature such as the boundries, edge, length of the shapes in the image By using this featues the reference image, test images are compared and objects are identified. The new similarity is learned iteratively so that the neighbors of a given shape influence its final similarity to the query This approach yields significant improvements over the state-of-art shape matching algorithms. This method obtained a retrieval rate of 91.61 percent on the MPEG-7 data set. They try to meet the online shape retrieval and classification demands This algorithm presented an extremely efficient shape matching approach based on compressed fourier coefficients. The proposed algorithm gives better retrieval rate compare to existing methods
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