VITALAS at TRECVID-2008

In this paper, we present our experiments in TRECVID 2008 about High-Level feature extraction task. This is the first year for our participation in TRECVID, our system adopts some popular approaches that other workgroups proposed before. We proposed 2 advanced low-level features NEW Gabor texture descriptor and the Compact-SIFT Codeword histogram. Our system applied well-known LIBSVM to train the SVM classifier for the basic classifier. In fusion step, some methods were employed such as the Voting, SVM-base, HCRF and Bootstrap Average AdaBoost(BAAB)

The Digital Earth Observation Librarian: A Data Mining Approach for Large Satellite Images Archives

Throughout the years, various Earth Observation (EO) satellites have generated huge amounts of data. The extraction of latent information in the data repositories is not a trivial task. New methodologies and tools, being capable of handling the size, complexity and variety of data, are required. Data scientists require support for the data manipulation, labeling and information extraction processes. This paper presents our Earth Observation Image Librarian (EOLib), a modular software framework which offers innovative image data mining capabilities for TerraSAR-X and EO image data, in general. The main goal of EOLib is to reduce the time needed to bring information to end-users from Payload Ground Segments (PGS). EOLib is composed of several modules which offer functionalities such as data ingestion, feature extraction from SAR (Synthetic Aperture Radar) data, meta-data extraction, semantic definition of the image content through machine learning and data mining methods, advanced querying of the image archives based on content, meta-data and semantic categories, as well as 3-D visualization of the processed images. EOLib is operated by DLR’s (German Aerospace Center’s) Multi-Mission Payload Ground Segment of its Remote Sensing Data Center at Oberpfaffenhofen, Germany

Image Annotation and Topic Extraction Using Super-Word Latent Dirichlet

This research presents a multi-domain solution that uses text and images to iteratively improve automated information extraction. Stage I uses local text surrounding an embedded image to provide clues that help rank-order possible image annotations. These annotations are forwarded to Stage II, where the image annotations from Stage I are used as highly-relevant super-words to improve extraction of topics. The model probabilities from the super-words in Stage II are forwarded to Stage III where they are used to refine the automated image annotation developed in Stage I. All stages demonstrate improvement over existing equivalent algorithms in the literature

A Learned Index for Exact Similarity Search in Metric Spaces

Indexing is an effective way to support efficient query processing in large databases. Recently the concept of learned index has been explored actively to replace or supplement traditional index structures with machine learning models to reduce storage and search costs. However, accurate and efficient similarity query processing in high-dimensional metric spaces remains to be an open challenge. In this paper, a novel indexing approach called LIMS is proposed to use data clustering and pivot-based data transformation techniques to build learned indexes for efficient similarity query processing in metric spaces. The underlying data is partitioned into clusters such that each cluster follows a relatively uniform data distribution. Data redistribution is achieved by utilizing a small number of pivots for each cluster. Similar data are mapped into compact regions and the mapped values are totally ordinal. Machine learning models are developed to approximate the position of each data record on the disk. Efficient algorithms are designed for processing range queries and nearest neighbor queries based on LIMS, and for index maintenance with dynamic updates. Extensive experiments on real-world and synthetic datasets demonstrate the superiority of LIMS compared with traditional indexes and state-of-the-art learned indexes.Comment: 14 pages, 14 figures, submitted to Transactions on Knowledge and Data Engineerin

The Google Similarity Distance

Words and phrases acquire meaning from the way they are used in society, from their relative semantics to other words and phrases. For computers the equivalent of `society' is `database,' and the equivalent of `use' is `way to search the database.' We present a new theory of similarity between words and phrases based on information distance and Kolmogorov complexity. To fix thoughts we use the world-wide-web as database, and Google as search engine. The method is also applicable to other search engines and databases. This theory is then applied to construct a method to automatically extract similarity, the Google similarity distance, of words and phrases from the world-wide-web using Google page counts. The world-wide-web is the largest database on earth, and the context information entered by millions of independent users averages out to provide automatic semantics of useful quality. We give applications in hierarchical clustering, classification, and language translation. We give examples to distinguish between colors and numbers, cluster names of paintings by 17th century Dutch masters and names of books by English novelists, the ability to understand emergencies, and primes, and we demonstrate the ability to do a simple automatic English-Spanish translation. Finally, we use the WordNet database as an objective baseline against which to judge the performance of our method. We conduct a massive randomized trial in binary classification using support vector machines to learn categories based on our Google distance, resulting in an a mean agreement of 87% with the expert crafted WordNet categories.Comment: 15 pages, 10 figures; changed some text/figures/notation/part of theorem. Incorporated referees comments. This is the final published version up to some minor changes in the galley proof

Efficient Point Clustering for Visualization

The visualization of large spatial point data sets constitutes a problem with respect to runtime and quality. A visualization of raw data often leads to occlusion and clutter and thus a loss of information. Furthermore, particularly mobile devices have problems in displaying millions of data items. Often, thinning via sampling is not the optimal choice because users want to see distributional patterns, cardinalities and outliers. In particular for visual analytics, an aggregation of this type of data is very valuable for providing an interactive user experience. This thesis defines the problem of visual point clustering that leads to proportional circle maps. It furthermore introduces a set of quality measures that assess different aspects of resulting circle representations. The Circle Merging Quadtree constitutes a novel and efficient method to produce visual point clusterings via aggregation. It is able to outperform comparable methods in terms of runtime and also by evaluating it with the aforementioned quality measures. Moreover, the introduction of a preprocessing step leads to further substantial performance improvements and a guaranteed stability of the Circle Merging Quadtree. This thesis furthermore addresses the incorporation of miscellaneous attributes into the aggregation. It discusses means to provide statistical values for numerical and textual attributes that are suitable for side-views such as plots and data tables. The incorporation of multiple data sets or data sets that contain class attributes poses another problem for aggregation and visualization. This thesis provides methods for extending the Circle Merging Quadtree to output pie chart maps or maps that contain circle packings. For the latter variant, this thesis provides results of a user study that investigates the methods and the introduced quality criteria. In the context of providing methods for interactive data visualization, this thesis finally presents the VAT System, where VAT stands for visualization, analysis and transformation. This system constitutes an exploratory geographical information system that implements principles of visual analytics for working with spatio-temporal data. This thesis details on the user interface concept for facilitating exploratory analysis and provides the results of two user studies that assess the approach