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BTU DBIS ’ Plant Identification Runs at
"... Abstract. In this work, we summarize the results of our first participa-tion in the plant identification task. Unlike other contributors, we present a rather untypical approach, which does not rely on classification techniques. In contrast, logical combina-tions of low-level features expressed in a ..."
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Abstract. In this work, we summarize the results of our first participa-tion in the plant identification task. Unlike other contributors, we present a rather untypical approach, which does not rely on classification techniques. In contrast, logical combina-tions of low-level features expressed in a query language are used to assess a document’s similarity to a species. Similar to ImageCLEF 2011, DBIS ’ approach is based on the commuting quantum query language (CQQL). CQQL was proposed by the workgroup to combine similar-ity predicates as found in information retrieval and relational predicates common in databases. In order to combine both predicate types, CQQL utilizes the mathematical formalisms of quantum mechanics and logic eventually forming a probabilistic logic. To test the utility of our query language, three different automatic ap-proaches are discussed. First, a query by example approach towards plant identification is presented. Second, the approach is combined with a k-medoid technique to exploit relationships within the top-k results. To conclude with, the aforementioned techniques are compared with the utilization of the k-medoid method alone. With respect to the non-existent experience with the task, the results of the discussed approach are fairly decent but leave room for improvement being outlined as future work.
BTU DBIS ’ Multimodal Wikipedia Retrieval Runs at ImageCLEF 2011
"... Abstract. In this work, we summarize the results of our first partici-pation in the Wikipedia Retrieval task. For our experiments, we rely on a cognitively motivated IR model: the principle of polyrepresentation. The principle’s core hypothesis is that a document is defined by different representati ..."
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Abstract. In this work, we summarize the results of our first partici-pation in the Wikipedia Retrieval task. For our experiments, we rely on a cognitively motivated IR model: the principle of polyrepresentation. The principle’s core hypothesis is that a document is defined by different representations such as low-level features, or textual content that can be combined in a structured manner reflecting the user’s information need. For our first participation, we used mono-lingual English retrieval in com-bination with global low-level features without further user interaction or query modification techniques. Our best NOFB reached rank 64 or rank 13 of the mono-lingual English runs. This result is promising as we have not used structural informa-tion about the documents. Additionally, our findings are indicating the correctness of the polyrepresentative hypothesis for multimodal retrieval.
BTU DBIS at ImageCLEF2013 Plant Identification Task
"... Abstract. In this paper we summarize the results of our second partic-ipation in the ImageCLEF2013 plant identification task. Again we used the combination of low-level features to identify similar pictures, identify adequate matchings and thus learn classifiers. This year, instead of us-ing our wor ..."
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Abstract. In this paper we summarize the results of our second partic-ipation in the ImageCLEF2013 plant identification task. Again we used the combination of low-level features to identify similar pictures, identify adequate matchings and thus learn classifiers. This year, instead of us-ing our workgroup’s similarity query language “Commuting Quantum Query Language ” (CQQL), we utilized support vector machines (SVMs) to classify the data. So we used classification on split subsets of the data instead of clustering similar results with the k-medoid method. For our experiments we used many different parameter combinations and feature combinations on the 2012 and 2013 data to compile four different runs.
BTU DBIS ’ Personal Photo Retrieval Runs at
"... Abstract. This paper summarizes the results of the BTU DBIS research group’s participation in the Personal Photo Retrieval subtask of Image-CLEF 2013. In order to solve the subtask, a self-developed multimodal multimedia retrieval system, PythiaSearch, is used. The discussed re-trieval approaches fo ..."
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Abstract. This paper summarizes the results of the BTU DBIS research group’s participation in the Personal Photo Retrieval subtask of Image-CLEF 2013. In order to solve the subtask, a self-developed multimodal multimedia retrieval system, PythiaSearch, is used. The discussed re-trieval approaches focus on two different strategies. First, two automatic approaches that combine visual features and meta data are examined. Second, a manually assisted relevance feedback approach is presented. All approaches are based on a special query language, CQQL, which supports the logical combination of different features. Considering only automatic runs without relevance feedback that have been submitted to the subtask, DBIS reached the best overall results, while the relevance feedback-assisted approach is placed second amongst all participants of the subtask.
