Theses and Dissertations
Permanent URI for this collectionhttp://ir.daiict.ac.in/handle/123456789/1
Browse
2 results
Search Results
Item Open Access Similarity preserving dimensionality reduction for image data(Dhirubhai Ambani Institute of Information and Communication Technology, 2016) Shikkenawis, Gitam; Mitra, Suman K.Data collection and storage capabilities have increased manifold in last few decades,leading to information overload. Number of variables used to represent each data observation is called dimension of the data and dealing with large dimensions is a challenging task. Images have become a source of such large data which is increasing day by day with advances in image capturing devices and demand of high resolution images. Images typically consist of large dimensions and processing that becomes very di cult even for machines. Dimensionality reduction techniques learn a compact representation of such data by exploring the properties such as correlation, pairwise distances, neighborhood structure etc. The idea is to retain these properties in lower dimensional representation as well, inducing minimum information loss. Early age techniques of dimensionality reduction preserve the global structure of the data, but,many a times, local manifold structure is more important than the global Euclidean structure. This thesis is an attempt to develop robust and powerful dimensionality reduction technique based on similarity preservation for image data. In particular,the thesis emphasizes on the dimensionality reduction techniques those are linear in nature and are based on preserving the local relationship of the image data.In this work, Locality Preserving Projection (LPP), that preserves the local structure of data is studied and its various extensions are proposed. LPP works on the concept that neighboring data points in the high dimensional space should remain neighbors in the low dimensional space as well. Ambiguities in regions having data points from di erent classes close by, less reducibility capacity, data dependent parameters, ignorance of discriminant information, non-orthogonality of the basis, vectorized processing are some of the issues with conventional LPP. Some of the variants of LPP have been introduced that try to resolve these problems. Discriminant information, if considered, can play vital role in obtaining separation between di erent classes. Variants of LPP, considering not only the local structure, but also the dissimilarity between the data points are proposed in the rst part of the thesis. Data representation, face and facial expression recognition experiments are performed using the proposed dimensionality reduction frameworks.Item Open Access Locality preserving projection: a study and applications(Dhirubhai Ambani Institute of Information and Communication Technology, 2012) Shikkenawis, Gitam; Mitra, Suman KLocality Preserving Projection (LPP) is a recently proposed approach for dimensionality reduction that preserves the neighbourhood information and obtains a subspace that best detects the essential data manifold structure. Currently it is widely used for finding the intrinsic dimensionality of the data which is usually of high dimension. This characteristic of LPP has made it popular among other available dimensionality reduction approaches such as Principal Component Analysis (PCA). A study on LPP reveals that it tries to preserve the information about nearest neighbours of data points, thus may lead to misclassification in the overlapping regions of two or more classes while performing data analysis. It has also been observed that the dimension reducibility capacity of conventional LPP is much less than that of PCA. A new proposal called Extended LPP (ELPP) which amicably resolves two issues mentioned above is introduced. In particular, a new weighing scheme is designed that pays importance to the data points which are at a moderate distance, in addition to the nearest points. This helps to resolve the ambiguity occurring at the overlapping regions as well as increase the reducibility capacity. LPP is used for a variety of applications for reducing the dimensions one of which is Face Recognition. Face Recognition is one of the most widely used biometric technology for person identification. Face images are represented as highdimensional pixel arrays and due to high correlation between the neighbouring pixel values; they often belong to an intrinsically low dimensional manifold. The distribution of data in a high dimensional space is non-uniform and is generally concentrated around some kind of low dimensional structures. Hence, one of the ways of performing Face Recognition is by reducing the dimensionality of the data and finding the subspace of the manifold in which face images reside. Both LPP and ELPP are used for Face and Expression Recognition tasks. As the aim is to separate the clusters in the embedded space, class membership information may add more discriminating power. With this in mind, the proposal is further extended to the supervised version of LPP (SLPP) that uses the known class labels of data points to enhance the discriminating power along with inheriting the properties of ELPP