Jukka Perkiö

A Scalable Topic-Based Open Source Search Engine

Site-based or topic-specific search engines work with mixed success because of the general difficulty of the information retrieval task, and the lack of good link information to allow authorities to be identified. We are advocating an open source approach to the problem due to its scope and need for software components. We have adopted a topic-based search engine because it represents the next generation of capability. This paper outlines our scalable system for site-based or topic-specific search, and demonstrates the developing system on a small 250,000 document collection of EU and UN web pages.

Long-term effects of ubiquitous surveillance in the home

The Helsinki Privacy Experiment is a study of the long-term effects of ubiquitous surveillance in homes. Ten volunteering households were instrumented with video cameras with microphones, and computer, wireless network, smartphone, TV, DVD, and customer card use was logged. We report on stress, anxiety, concerns, and privacy-seeking behavior after six months. The data provide first insight into the privacy-invading character of ubiquitous surveillance in the home and explain how people can gradually become accustomed to surveillance even if they oppose it.

Exploring Independent Trends in a Topic-Based Search Engine

Topic-based search engines are an alternative to simple keyword search engines that are common in todays intranets. The temporal behaviour of the topics in a topic model based search engine can be used for trend analysis, which is an important research goal on its own. We apply topic modelling to an online financial newspaper data and show that some of the trends in the topics are consistent with common understanding.

Modelling Image Complexity by Independent Component Analysis, with Application to Content-Based Image Retrieval

Estimating the degree of similarity between images is a challenging task as the similarity always depends on the context. Because of this context dependency, it seems quite impossible to create a universal metric for the task. The number of low-level features on which the judgement of similarity is based may be rather low, however. One approach to quantifying the similarity of images is to estimate the (joint) complexity of images based on these features. We present a novel method to estimate the complexity of images, based on ICA. We further use this to model joint complexity of images, which gives distances that can be used in content-based retrieval. We compare this new method to two other methods, namely estimating mutual information of images using marginal Kullback-Leibler divergence and approximating the Kolmogorov complexity of images using Normalized Compression Distance.

Multi-faceted information retrieval system for large scale email archives

We profile a system for search and analysis of large-scale email archives. The system builds around four facets: Content-based search engine, statistical topic model, automatically inferred social networks and time-series analysis. The facets correspond to the types of information available in email data. The presented system allows chaining or combining the facets flexibly. Results of one facet may be used as input to another, yielding remarkable combinatorial power. In information retrieval point of view, the system provides support for exploration, approximate textual searches and data visualization. We present some experimental results based on a large real-world email corpus.

Utilizing Rich Bluetooth Environments for Identity Prediction and Exploring Social Networks as Techniques for Ubiquitous Computing

Personal identification and using that information is in the heart of many ubiquitous systems. We present two complementary techniques, namely personal identification without directly observing the subject, and using that information for understanding the social relations between the subjects. We show that with certain presumptions it is possible to predict ones identity with reasonable certainty only by observing ones Bluetooth neighborhood without the need to directly observe the subject. We also show how this information can be used for exploring the social relations between the subjects

A temporally adaptive content-based relevance ranking algorithm

In information retrieval relevance ranking of the results is one of the most important single tasks there are. There are many diffierent ranking algorithms based on the content of the documents or on some external properties e.g. link structure of html documents.We present a temporally adaptive content-based relevance ranking algorithm that explicitly takes into account the temporal behavior of the underlying statistical properties of the documents in the form of a statistical topic model. more we state that our algorithm can be used on top of any ranking algorithm.

Multi-Faceted Information Retrieval System for Large Scale Email Archives

We profile a system for search and analysis of large-scale email archives. The system builds around four facets: content-based search engine, statistical topic model, automatically inferred social networks, and time-series analysis. The facets correspond to the types of information available in email data. The presented system allows chaining or combining the facets flexibly. Results of one facet may be used as input to another yielding remarkable combinatorial power. In information retrieval point of view, the system provides support for exploration, approximate textual searches and data visualization. We present some experimental results based on a large real-world email corpus.

Image Similarity: From Syntax to Weak Semantics Using Multimodal Features with Application to Multimedia Retrieval

Measuring image similarity is an important task for various multimedia applications. Similarity can be defined at two levels: at the syntactic (lower, context-free) level and at the semantic (higher, contextual) level. As long as one deals with the syntactic level, defining and measuring similarity is a relatively straightforward task, but as soon as one starts dealing with the semantic similarity, the task becomes very difficult. We examine the use of very simple syntactic image features combined with other multimodal features to derive a similarity measure that captures the weak semantics of an image. We test and further use this similarity measure to do video retrieval.

Image similarity: from syntax to weak semantics

Measuring image similarity is an important task for various multimedia applications. Similarity can be defined at two levels: at the syntactic (lower, context-free) level and at the semantic (higher, contextual) level. As long as one deals with the syntactic level, defining and measuring similarity is a relatively straightforward task, but as soon as one starts dealing with the semantic similarity, the task becomes very difficult. We examine the use of simple readily available syntactic image features combined with other multimodal features to derive a similarity measure that captures the weak semantics of an image. The weak semantics can be seen as an intermediate step between low level image understanding and full semantic image understanding. We investigate the use of single modalities alone and see how the combination of modalities affect the similarity measures. We also test the measure on multimedia retrieval task on a tv series data, even though the motivation is in understanding how different modalities relate to each other.