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Locality preserving projections thesis writing

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  • Gitam Shikkenawis
  • Suman K. Mitra . . www
  • Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, India

Received 1 July 2014. Revised 10 November 2014. Accepted 15 January 2015. Available online 6 September 2015.

Abstract

High dimensional data is hard to interpret and work with in its raw form; hence dimensionality reduction is applied beforehand to discover underlying low dimensional manifold. Locality Preserving Projection (LPP) was introduced using the concept that neighboring data points in the high dimensional space should remain neighbors in the low dimensional space as well. In a typical pattern recognition problem, true neighbors are defined as the patterns belonging to same class. Ambiguities in regions having data points from different classes close by, less reducibility capacity and data dependent parameters are some of the issues with conventional LPP. In this article, some of the variants of LPP have been introduced that try to resolve these problems. A weighing function that tunes the parameters depending on data and takes care of the other issues is used in Extended version of LPP (ELPP). Better class discrimination is obtained using the concept of intra and inter-class distance in a supervised variant (ESLPP-MD). To capture the non-linearity of the data, Kernel based variants are used, that first map the data to feature space. Data representation, clustering, face and facial expression recognition performances are reported on a large set of databases.

Keywords

  • Locality preserving projection
  • Subspace analysis
  • Dimensionality reduction
  • Data discrimination

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Vitae

Gitam Shikkenawis is a Ph.D. Scholar at Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar and has received TCS Research Fellowship, 2013. Gitam obtained her M.Tech. in Machine Intelligence from Dhirubhai Ambani Institute of Information and Communication Technology (DAIICT), Gandhinagar, India. She received her B.E. degree in Computer Science from G. H. Patel College of Engineering and Technology, V.V. Nagar, India, in 2010. She is currently working on dimensionality reduction and image restoration. Her area of interest is Pattern Recognition and Image Processing. Gitam was a recipient of Google India Women in Engineering Award, 2011.

Suman K. Mitra is a Professor at the Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar. Dr. Mitra obtained his Ph.D. from the Indian Statistical Institute, Calcutta. Earlier to his current position, Dr. Mitra was with the Institute of Neural Computation at the University of California, San Diego, USA and with the Department of Mathematics at the Indian Institute of Technology, Bombay. Dr. Mitra has 70 technical papers published in various prestigious National and International Journals and Conferences. He also has 2 US patents in his credit. Dr. Mitra׳s research interest includes image processing, pattern recognition, Bayesian networks and digital watermarking. Currently, Dr. Mitra is serving International Journal of Image and Graphics (IJIG) as an Associate Editor.

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Dr. Mitra is a Senior Member of IEEE.

Corresponding author at: DA-IICT Post Bag 4, Near Indroda Circle, Gandhinagar 382007, Gujarat, India. Tel. +91 79 30510584; fax: +91 79 30520010.

2015 Elsevier B.V. All rights reserved.

Citing articles ( )

The aim of object detection is to recognize objects in a visual scene. Performing reliable object detection is becoming increasingly important in the fields of computer vision and robotics. Various applications of object detection include video surveillance, traffic monitoring, digital libraries, navigation, human computer interaction, etc. The challenges involved with detecting real world objects include the multitude of colors, textures, sizes, and cluttered or complex backgrounds making objects difficult to detect.

This thesis contributes to the exploration of various dimensionality reduction techniques on descriptors for establishing an object detection system that achieves the best trade-offs between performance and speed. Histogram of Oriented Gradients (HOG) and other histogram-based descriptors were used as an input to a Support Vector Machine (SVM) classifier to achieve good classification performance. Binary descriptors were considered as a computationally efficient alternative to HOG. It was determined that single local binary descriptors in combination with Support Vector Machine (SVM) classifier don’t work as well as histograms of features for object detection. Thus, histogram of binary descriptors features were explored as a viable alternative and the results were found to be comparable to those of the popular Histogram of Oriented Gradients descriptor.

Histogram-based descriptors can be high dimensional and working with large amounts of data can be computationally expensive and slow. Thus, various dimensionality reduction techniques were considered, such as principal component analysis (PCA), which is the most widely used technique, random projections, which is data independent and fast to compute, unsupervised locality preserving projections (LPP), and supervised locality preserving projections (SLPP), which incorporate non-linear reduction techniques.

The classification system was tested on eye detection as well as different object classes. The eye database was created using BioID and FERET databases. Additionally, the CalTech-101 data set, which has 101 object categories, was used to evaluate the system. The results showed that the reduced-dimensionality descriptors based on SLPP gave improved classification performance with fewer computations.

Computer vision; Pattern recognition systems; Dimension reduction (Statistics)

Computer Engineering (MS)

Computer Engineering (KGCOE)

Shanchieh Jay Yang

Physical copy available from RIT’s Wallace Library at TA1634 .S43 2014

Sharma, Riti, “Object Detection using Dimensionality Reduction on Image Descriptors” (2014). Thesis. Rochester Institute of Technology. Accessed from
scholarworks.rit.edu/theses/8211


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