TRECVID 2005 Experiments at Media Team Oulu

University of Oulu’s MediaTeam research group participated to the manual and interactive search tasks with our video retrieval system, which is constructed out of three sub-applications: Content-based Query Tool, Cluster-Temporal Browser and Result Container with relevance feedback mechanism. The search engine in the heart of our search system creates result sets using three distinct search sub-engines that take care of the text, semantic concept and example based queries. The sub-applications and subengines result in varying search strategies. Therefore their effect on overall search performance has been studied in our experiments. The principal contribution in our search system is the Cluster-Temporal Browser, the effect of which we tested this year in conjunction with the traditional content-based paradigm: recurrent queries with relevance feedback. Seven search runs were submitted for the TRECVID 2005 search task: I_A_2_OUMT_I1Q_1: interactive with browsing disabled, expert users I_A_2_OUMT_I2B_2: interactive with browsing enabled, expert users I_A_2_OUMT_I3Q_3: interactive with browsing disabled, novice users I_A_2_OUMT_I4B_4: interactive with browsing enabled, novice users M_A_1_OUMT_M5T_5: manual with official text transcripts M_A_2_OUMT_M6TS_6: manual with official text transcripts and selected semantic concepts M_A_2_OUMT_M7TE_7: manual with official text transcripts and selected topic examples For interactive experiments, we have used eight test users: four novice and four expert users. In a carefully designed experimental setup, all users have used two system configurations: one with and one without Cluster-Temporal Browser. In our experiments we found that by incorporating Cluster-Temporal Browser the performance gain is more than 12% over the conventional content-based tools with novice users. We also found that on average expert users gained more than 18% better search performance over novice users, which shows that the test design has a significant effect to the outcome of the interactive test. Our manual experiment is a comparison of three query configurations: a baseline text only search, text combined with visual example search and text combined with semantic concept search. To facilitate a concept search, we trained a set of concept detectors (to name a few: newsroom, entertainment, maps and charts, news footage, weather, sports, outdoors). Our example search was based on low level descriptions (color and structure). The goal of the experiment was to find out how much more visual search examples and user defined semantic concepts contribute to the search performance over a text baseline. Our experimental results showed that the combined text and semantic concept search gives about 19% better performance over text baseline whereas text combined with example based search gives about 25% performance gain over the baseline. The results show that specific visual search examples accumulate better overall precision than the queries defined with our detected set of semantic concepts.