Video databases annotation enhancing using commonsense knowledgebases for indexing and retrieval

The rapidly increasing amount of video collections, especially on the web, motivated the need for intelligent automated annotation tools for searching, rating, indexing and retrieval purposes. These videos collections contain all types of manually annotated videos. As this annotation is usually incomplete and uncertain and contains misspelling words, search using some keywords almost do retrieve only a portion of videos which actually contains the desired meaning. Hence, this annotation needs filtering, expanding and validating for better indexing and retrieval. In this paper, we present a novel framework for video annotation enhancement, based on merging two widely known commonsense knowledgebases, namely WordNet and ConceptNet. In addition to that, a comparison between these knowledgebases in video annotation domain is presented. Experiments were performed on random wide-domain video clips, from the \emph{vimeo.com} website. Results show that searching for a video over enhanced tags, based on our proposed framework, outperforms searching using the original tags. In addition to that, the annotation enhanced by our framework outperforms both those enhanced by WordNet and ConceptNet individually, in terms of tags enrichment ability, concept diversity and most importantly retrieval performance

DCU@FIRE2010: term conflation, blind relevance feedback, and cross-language IR with manual and automatic query translation

For the first participation of Dublin City University (DCU) in the FIRE 2010 evaluation campaign, information retrieval (IR) experiments on English, Bengali, Hindi, and Marathi documents were performed to investigate term conation (different stemming approaches and indexing word prefixes), blind relevance feedback, and manual and automatic query translation. The experiments are based on BM25 and on language modeling (LM) for IR. Results show that term conation always improves mean average precision (MAP) compared to indexing unprocessed word forms, but different approaches seem to work best for different languages. For example, in monolingual Marathi experiments indexing 5-prefixes outperforms our corpus-based stemmer; in Hindi, the corpus-based stemmer achieves a higher MAP. For Bengali, the LM retrieval model achieves a much higher MAP than BM25 (0.4944 vs. 0.4526). In all experiments using BM25, blind relevance feedback yields considerably higher MAP in comparison to experiments without it. Bilingual IR experiments (English!Bengali and English!Hindi) are based on query translations obtained from native speakers and the Google translate web service. For the automatically translated queries, MAP is slightly (but not significantly) lower compared to experiments with manual query translations. The bilingual English!Bengali (English!Hindi) experiments achieve 81.7%-83.3% (78.0%-80.6%) of the best corresponding monolingual experiments

Advanced content-based semantic scene analysis and information retrieval: the SCHEMA project

The aim of the SCHEMA Network of Excellence is to bring together a critical mass of universities, research centers, industrial partners and end users, in order to design a reference system for content-based semantic scene analysis, interpretation and understanding. Relevant research areas include: content-based multimedia analysis and automatic annotation of semantic multimedia content, combined textual and multimedia information retrieval, semantic -web, MPEG-7 and MPEG-21 standards, user interfaces and human factors. In this paper, recent advances in content-based analysis, indexing and retrieval of digital media within the SCHEMA Network are presented. These advances will be integrated in the SCHEMA module-based, expandable reference system

Context Based Indexing On Synonym System Using Hierarchical Clustering In Web Mining

Now a days, the World Wide Web is the collection of large amount of information which is increasing day by day. For this increasing amount of information, there is a need for efficient and effective indexing structure. Indexing in search engines has become the major issue for improving the performance of Web search engines, so that the most relevant web documents are retrieved in minimum possible time. For this a new indexing mechanism in search engine is proposed which is based on indexing the synonym terms of the web documents, a synonym term which have multiple context with same meaning of the web documents. The indexing is performed on the bases of hierarchical clustering method which clustered the similar term documents into the same cluster and these clusters are clubbed together to form mega cluster on the basis of synonym term. With the similarity of clusters, it will optimize the search process by forming the different levels of hierarchy. Finally, it will give fast and relevant retrieval of web documents to the user

Development of an Enhanced Knowledge Retrieval System Using Web 2.0 Technology and Vector Space Model

There is an increasing pool of information on the web and a major contributor is web 2.0 technology on which social media is based. Searching for specific information in this pool is always tasking, therefore, the need to harness this information as a means of enhancing retrieval and reuse of relevant ones. Some researches and development have been carried out in the field of Knowledge Retrieval using Vector Space Model (VSM) and Latent Semantic Indexing (LSI), but the approach used is based on large pool of information available online, which makes getting most relevant information relatively difficult at the point of retrieval, this is a major setback. Collaborations on Facebook and Twitter (web 2.0 technology) were harvested using APIs and stored in the Knowledge Repository, The collaboration on social media served as the source of information in the Knowledge Repository. An Enhanced Knowledge Retrieval System (EKRS) applying VSM was developed and implemented. The use of VSM was to calculate the Cosine Similarity and Term Frequency to aid effective retrieval of relevant documents from the repository based on user’s needs. In this project, we were able to achieve the aim of retrieving relevant documents. EKRS was able to employ both web 2.0 and VSM to meet specific user’s information needs. Keywords: web 2.0, Knowledge retrieval, Vector Space Model, Latent Semantic Indexing, Knowledge Repository, Cosine Similarity and Term Frequency

A news video retrieval framework for the study of implicit relevance feedback.

In this paper, we propose a framework for recording, analysing, indexing and retrieving news videos such as the BBC one o'clock news. We believe that such a framework will be useful to identify implicit indicators of relevance, a nearly untouched area in adaptive multimedia retrieval. Due to its advantages as a Web application and its up-to-date content, it can be a promising approach to motivate a broad quantity of users to interact with the system