Embedding Directed Graphs in Potential Fields Using FastMap-D

Embedding undirected graphs in a Euclidean space has many computational benefits. FastMap is an efficient embedding algorithm that facilitates a geometric interpretation of problems posed on undirected graphs. However, Euclidean distances are inherently symmetric and, thus, Euclidean embeddings cannot be used for directed graphs. In this paper, we present FastMap-D, an efficient generalization of FastMap to directed graphs. FastMap-D embeds vertices using a potential field to capture the asymmetry between the pairwise distances in directed graphs. FastMap-D learns a potential function to define the potential field using a machine learning module. In experiments on various kinds of directed graphs, we demonstrate the advantage of FastMap-D over other approaches.Comment: 9 pages, Published in Symposium on Combinatorial Search(SoCS-2020). Erratum with updated Result

Combining diversity queries and visual mining to improve content-based image retrieval systems: the DiVI method

This paper proposes a new approach to improve similarity queries with diversity, the Diversity and Visually-Interactive method (DiVI), which employs Visual Data Mining techniques in Content-Based Image Retrieval (CBIR) systems. DiVI empowers the user to understand how the measures of similarity and diversity affect their queries, as well as increases the relevance of CBIR results according to the user judgment. An overview of the image distribution in the database is shown to the user through multidimensional projection. The user interacts with the visual representation changing the projected space or the query parameters, according to his/her needs and previous knowledge. DiVI takes advantage of the users’ activity to transparently reduce the semantic gap faced by CBIR systems. Empirical evaluation show that DiVI increases the precision for querying by content and also increases the applicability and acceptance of similarity with diversity in CBIR systems.FAPESPCNPqCAPESRescuer Project (European Commission Grant 614154 and CNPq/MCTI Grant 490084/2013-3

Efficient Anonymous Biometric Matching in Privacy-Aware Environments

Video surveillance is an important tool used in security and environmental monitoring, however, the widespread deployment of surveillance cameras has raised serious privacy concerns. Many privacy-enhancing schemes have been recently proposed to automatically redact images of selected individuals in the surveillance video for protection. To identify these individuals for protection, the most reliable approach is to use biometric signals as they are immutable and highly discriminative. If misused, these characteristics of biometrics can seriously defeat the goal of privacy protection. In this dissertation, an Anonymous Biometric Access Control (ABAC) procedure is proposed based on biometric signals for privacy-aware video surveillance. The ABAC procedure uses Secure Multi-party Computational (SMC) based protocols to verify membership of an incoming individual without knowing his/her true identity. To make SMC-based protocols scalable to large biometric databases, I introduce the k-Anonymous Quantization (kAQ) framework to provide an effective and secure tradeoff of privacy and complexity. kAQ limits systems knowledge of the incoming individual to k maximally dissimilar candidates in the database, where k is a design parameter that controls the amount of complexity-privacy tradeoff. The relationship between biometric similarity and privacy is experimentally validated using a twin iris database. The effectiveness of the entire system is demonstrated based on a public iris biometric database. To provide the protected subjects with full access to their privacy information in video surveillance system, I develop a novel privacy information management system that allows subjects to access their information via the same biometric signals used for ABAC. The system is composed of two encrypted-domain protocols: the privacy information encryption protocol encrypts the original video records using the iris pattern acquired during ABAC procedure; the privacy information retrieval protocol allows the video records to be anonymously retrieved through a GC-based iris pattern matching process. Experimental results on a public iris biometric database demonstrate the validity of my framework

Signal and image processing methods for imaging mass spectrometry data

Imaging mass spectrometry (IMS) has evolved as an analytical tool for many biomedical applications. This thesis focuses on algorithms for the analysis of IMS data produced by matrix assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometer. IMS provides mass spectra acquired at a grid of spatial points that can be represented as hyperspectral data or a so-called datacube. Analysis of this large and complex data requires efficient computational methods for matrix factorization and for spatial segmentation. In this thesis, state of the art processing methods are reviewed, compared and improved versions are proposed. Mathematical models for peak shapes are reviewed and evaluated. A simulation model for MALDI-TOF is studied, expanded and developed into a simulator for 2D or 3D MALDI-TOF-IMS data. The simulation approach paves way to statistical evaluation of algorithms for analysis of IMS data by providing a gold standard dataset. [...

NNMap: A method to construct a good embedding for nearest neighbor classification

a b s t r a c t This paper aims to deal with the practical shortages of nearest neighbor classifier. We define a quantitative criterion of embedding quality assessment for nearest neighbor classification, and present a method called NNMap to construct a good embedding. Furthermore, an efficient distance is obtained in the embedded vector space, which could speed up nearest neighbor classification. The quantitative quality criterion is proposed as a local structure descriptor of sample data distribution. Embedding quality corresponds to the quality of the local structure. In the framework of NNMap, one-dimension embeddings act as weak classifiers with pseudo-losses defined on the amount of the local structure preserved by the embedding. Based on this property, the NNMap method reduces the problem of embedding construction to the classical boosting problem. An important property of NNMap is that the embedding optimization criterion is appropriate for both vector and non-vector data, and equally valid in both metric and non-metric spaces. The effectiveness of the new method is demonstrated by experiments conducted on the MNIST handwritten dataset, the CMU PIE face images dataset and the datasets from UCI machine learning repository

Indexing methods for multimedia data objects given pair-wise distances.

by Chan Mei Shuen Polly.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 67-70).Abstract --- p.iiAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Definitions --- p.3Chapter 1.2 --- Thesis Overview --- p.5Chapter 2 --- Background and Related Work --- p.6Chapter 2.1 --- Feature-Based Index Structures --- p.6Chapter 2.2 --- Distance Preserving Methods --- p.8Chapter 2.3 --- Distance-Based Index Structures --- p.9Chapter 2.3.1 --- The Vantage-Point Tree Method --- p.10Chapter 3 --- The Problem of Distance Preserving Methods in Querying --- p.12Chapter 3.1 --- Some Experimental Results --- p.13Chapter 3.2 --- Discussion --- p.15Chapter 4 --- Nearest Neighbor Search in VP-trees --- p.17Chapter 4.1 --- The sigma-factor Algorithm --- p.18Chapter 4.2 --- The Constant-α Algorithm --- p.22Chapter 4.3 --- The Single-Pass Algorithm --- p.24Chapter 4.4 --- Discussion --- p.25Chapter 4.5 --- Performance Evaluation --- p.26Chapter 4.5.1 --- Experimental Setup --- p.27Chapter 4.5.2 --- Results --- p.28Chapter 5 --- Update Operations on VP-trees --- p.41Chapter 5.1 --- Insert --- p.41Chapter 5.2 --- Delete --- p.48Chapter 5.3 --- Performance Evaluation --- p.51Chapter 6 --- Minimizing Distance Computations --- p.57Chapter 6.1 --- A Single Vantage Point per Level --- p.58Chapter 6.2 --- Reuse of Vantage Points --- p.59Chapter 6.3 --- Performance Evaluation --- p.60Chapter 7 --- Conclusions and Future Work --- p.63Chapter 7.1 --- Future Work --- p.65Bibliography --- p.6