ImageSieve: Exploratory search of museum archives with named entity-based faceted browsing

Over the last few years, faceted search emerged as an attractive alternative to the traditional "text box" search and has become one of the standard ways of interaction on many e-commerce sites. However, these applications of faceted search are limited to domains where the objects of interests have already been classified along several independent dimensions, such as price, year, or brand. While automatic approaches to generate faceted search interfaces were proposed, it is not yet clear to what extent the automatically-produced interfaces will be useful to real users, and whether their quality can match or surpass their manually-produced predecessors. The goal of this paper is to introduce an exploratory search interface called ImageSieve, which shares many features with traditional faceted browsing, but can function without the use of traditional faceted metadata. ImageSieve uses automatically extracted and classified named entities, which play important roles in many domains (such as news collections, image archives, etc.). We describe one specific application of ImageSieve for image search. Here, named entities extracted from the descriptions of the retrieved images are used to organize a faceted browsing interface, which then helps users to make sense of and further explore the retrieved images. The results of a user study of ImageSieve demonstrate that a faceted search system based on named entities can help users explore large collections and find relevant information more effectively

Exploratory Analysis of Highly Heterogeneous Document Collections

We present an effective multifaceted system for exploratory analysis of highly heterogeneous document collections. Our system is based on intelligently tagging individual documents in a purely automated fashion and exploiting these tags in a powerful faceted browsing framework. Tagging strategies employed include both unsupervised and supervised approaches based on machine learning and natural language processing. As one of our key tagging strategies, we introduce the KERA algorithm (Keyword Extraction for Reports and Articles). KERA extracts topic-representative terms from individual documents in a purely unsupervised fashion and is revealed to be significantly more effective than state-of-the-art methods. Finally, we evaluate our system in its ability to help users locate documents pertaining to military critical technologies buried deep in a large heterogeneous sea of information.Comment: 9 pages; KDD 2013: 19th ACM SIGKDD Conference on Knowledge Discovery and Data Minin

A Comprehensive Review on Multimedia Retrieval Techniques

Abstract: With the prevalence of sight and sound advancements and web mediums, client can't fulfil with the customarey techniques for data retrieval systems. On account of this, the substance based picture recovery is turning into another and quick strategy for data recovery. Substance based picture recovery is the system for recovering the information especially pictures from a wide gathering of databases. The recovery is careried out by utilizing highlights. Content Based Image Retrieval (CBIR) is a system to compose the wide mixture of pictures by their visual highlight. Feature based recovery or retrieval procedures aree accessible for recovering the pictures, in our review we aree investigating them. In our first segment, we aree tending towareds a few nuts and bolts of a specific CBIR framework with that we have demonstrated some fundamental highlights of any picture, these aree similare to shape, surface, shading and indicated diverse systems to compute them. We have also demonstrated diverse separeation measuring systems utilized for closeness estimation of any picture furthermore talked about indexing methods. At last conclusion and future degree is examined. DOI: 10.17762/ijritcc2321-8169.15061

Ontology summarization: an analysis and an evaluation

Ontology summarization has been recognized as a very useful technique to facilitate ontology understanding and then support ontology reuse as a new or supplementing technique. A number of efforts have emerged lately that apply different criteria, addressing different features of ontology, to extract ontology summaries. However, those efforts are ad-hoc in that there lacks consensus on a number of issues fundamental to the development of the field, such as a definition for ontology summarization, use case scenarios etc. Also, there lack sufficient evaluations and analysis, e.g. comparison among them and with other similar techniques, to provide meaning guidelines for users of this technique. With the aim to provide solutions to those fundamental issues, in this work, we present an analysis of this technique and its approaches. With the help of an objective evaluation method, we investigate what features of ontology are important in ontology summarization

A gamification-based approach on indoor wayfinding research

Indoor environments can be very complex. Due to the challenges in these environments in combination with the absence of mobile wayfinding aids, a great need exists for innovative research on indoor wayfinding. In this explorative study, a game was developed in Unity to investigate whether the concept of gamification could be used in studies on indoor wayfinding so as to provide useful information regarding the link between wayfinding performance, personal characteristics, and building layout. Results show a significant difference between gamers and non-gamers as the complexity of the player movement has an important impact on the navigation velocity in the game. However, further analysis reveals that the architectural layout also has an impact on the navigation velocity and that wrong turns in the game are influenced by the landmarks at the decision points: navigating at deeper decision points in convex spaces is slower and landmarks of the categories pictograms and infrastructural were more effective in this particular building. Therefore, this explorative study, which provides an approach for the use of gamification in indoor wayfinding research, has shown that serious games could be successfully used as a medium for data acquisition related to indoor wayfinding in a virtual environment

A pattern mining approach for information filtering systems

It is a big challenge to clearly identify the boundary between positive and negative streams for information filtering systems. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on the RCV1 data collection, and substantial experiments show that the proposed approach achieves encouraging performance and the performance is also consistent for adaptive filtering as well