An affect-based video retrieval system with open vocabulary querying

Content-based video retrieval systems (CBVR) are creating new search and browse capabilities using metadata describing significant features of the data. An often overlooked aspect of human interpretation of multimedia data is the affective dimension. Incorporating affective information into multimedia metadata can potentially enable search using this alternative interpretation of multimedia content. Recent work has described methods to automatically assign affective labels to multimedia data using various approaches. However, the subjective and imprecise nature of affective labels makes it difficult to bridge the semantic gap between system-detected labels and user expression of information requirements in multimedia retrieval. We present a novel affect-based video retrieval system incorporating an open-vocabulary query stage based on WordNet enabling search using an unrestricted query vocabulary. The system performs automatic annotation of video data with labels of well defined affective terms. In retrieval annotated documents are ranked using the standard Okapi retrieval model based on open-vocabulary text queries. We present experimental results examining the behaviour of the system for retrieval of a collection of automatically annotated feature films of different genres. Our results indicate that affective annotation can potentially provide useful augmentation to more traditional objective content description in multimedia retrieval

A Case Study in Matching Service Descriptions to Implementations in an Existing System

A number of companies are trying to migrate large monolithic software systems to Service Oriented Architectures. A common approach to do this is to first identify and describe desired services (i.e., create a model), and then to locate portions of code within the existing system that implement the described services. In this paper we describe a detailed case study we undertook to match a model to an open-source business application. We describe the systematic methodology we used, the results of the exercise, as well as several observations that throw light on the nature of this problem. We also suggest and validate heuristics that are likely to be useful in partially automating the process of matching service descriptions to implementations.Comment: 20 pages, 19 pdf figure

Examining and improving the effectiveness of relevance feedback for retrieval of scanned text documents

Important legacy paper documents are digitized and collected in online accessible archives. This enables the preservation, sharing, and significantly the searching of these documents. The text contents of these document images can be transcribed automatically using OCR systems and then stored in an information retrieval system. However, OCR systems make errors in character recognition which have previously been shown to impact on document retrieval behaviour. In particular relevance feedback query-expansion methods, which are often effective for improving electronic text retrieval, are observed to be less reliable for retrieval of scanned document images. Our experimental examination of the effects of character recognition errors on an ad hoc OCR retrieval task demonstrates that, while baseline information retrieval can remain relatively unaffected by transcription errors, relevance feedback via query expansion becomes highly unstable. This paper examines the reason for this behaviour, and introduces novel modifications to standard relevance feedback methods. These methods are shown experimentally to improve the effectiveness of relevance feedback for errorful OCR transcriptions. The new methods combine similar recognised character strings based on term collection frequency and a string edit-distance measure. The techniques are domain independent and make no use of external resources such as dictionaries or training data

Personalized content retrieval in context using ontological knowledge

Personalized content retrieval aims at improving the retrieval process by taking into account the particular interests of individual users. However, not all user preferences are relevant in all situations. It is well known that human preferences are complex, multiple, heterogeneous, changing, even contradictory, and should be understood in context with the user goals and tasks at hand. In this paper, we propose a method to build a dynamic representation of the semantic context of ongoing retrieval tasks, which is used to activate different subsets of user interests at runtime, in a way that out-of-context preferences are discarded. Our approach is based on an ontology-driven representation of the domain of discourse, providing enriched descriptions of the semantics involved in retrieval actions and preferences, and enabling the definition of effective means to relate preferences and context

Automatic tagging and geotagging in video collections and communities

Automatically generated tags and geotags hold great promise to improve access to video collections and online communi- ties. We overview three tasks offered in the MediaEval 2010 benchmarking initiative, for each, describing its use scenario, definition and the data set released. For each task, a reference algorithm is presented that was used within MediaEval 2010 and comments are included on lessons learned. The Tagging Task, Professional involves automatically matching episodes in a collection of Dutch television with subject labels drawn from the keyword thesaurus used by the archive staff. The Tagging Task, Wild Wild Web involves automatically predicting the tags that are assigned by users to their online videos. Finally, the Placing Task requires automatically assigning geo-coordinates to videos. The specification of each task admits the use of the full range of available information including user-generated metadata, speech recognition transcripts, audio, and visual features

Intelligent indexing of crime scene photographs

The Scene of Crime Information System's automatic image-indexing prototype goes beyond extracting keywords and syntactic relations from captions. The semantic information it gathers gives investigators an intuitive, accurate way to search a database of cases for specific photographic evidence. Intelligent, automatic indexing and retrieval of crime scene photographs is one of the main functions of SOCIS, our research prototype developed within the Scene of Crime Information System project. The prototype, now in its final development and evaluation phase, applies advanced natural language processing techniques to text-based image indexing and retrieval to tackle crime investigation needs effectively and efficiently