DCU and UTA at ImageCLEFPhoto 2007

Dublin City University (DCU) and University of Tampere(UTA) participated in the ImageCLEF 2007 photographic ad-hoc retrieval task with several monolingual and bilingual runs. Our approach was language independent: text retrieval based on fuzzy s-gram query translation was combined with visual retrieval. Data fusion between text and image content was performed using unsupervised query-time weight generation approaches. Our baseline was a combination of dictionary-based query translation and visual retrieval, which achieved the best result. The best mixed modality runs using fuzzy s-gram translation achieved on average around 83% of the performance of the baseline. Performance was more similar when only top rank precision levels of P10 and P20 were considered. This suggests that fuzzy sgram query translation combined with visual retrieval is a cheap alternative for cross-lingual image retrieval where only a small number of relevant items are required. Both sets of results emphasize the merit of our query-time weight generation schemes for data fusion, with the fused runs exhibiting marked performance increases over single modalities, this is achieved without the use of any prior training data

Measuring the impact of temporal context on video retrieval

In this paper we describe the findings from the K-Space interactive video search experiments in TRECVid 2007, which examined the effects of including temporal context in video retrieval. The traditional approach to presenting video search results is to maximise recall by offering a user as many potentially relevant shots as possible within a limited amount of time. ‘Context’-oriented systems opt to allocate a portion of theresults presentation space to providing additional contextual cues about the returned results. In video retrieval these cues often include temporal information such as a shot’s location within the overall video broadcast and/or its neighbouring shots. We developed two interfaces with identical retrieval functionality in order to measure the effects of such context on user performance. The first system had a ‘recall-oriented’ interface, where results from a query were presented as a ranked list of shots. The second was ‘contextoriented’, with results presented as a ranked list of broadcasts. 10 users participated in the experiments, of which 8 were novices and 2 experts. Participants completed a number of retrieval topics using both the recall-oriented and context-oriented systems

TRECVid 2007 experiments at Dublin City University

In this paper we describe our retrieval system and experiments performed for the automatic search task in TRECVid 2007. We submitted the following six automatic runs: • F A 1 DCU-TextOnly6: Baseline run using only ASR/MT text features. • F A 1 DCU-ImgBaseline4: Baseline visual expert only run, no ASR/MT used. Made use of query-time generation of retrieval expert coefficients for fusion. • F A 2 DCU-ImgOnlyEnt5: Automatic generation of retrieval expert coefficients for fusion at index time. • F A 2 DCU-imgOnlyEntHigh3: Combination of coefficient generation which combined the coefficients generated by the query-time approach, and the index-time approach, with greater weight given to the index-time coefficient. • F A 2 DCU-imgOnlyEntAuto2: As above, except that greater weight is given to the query-time coefficient that was generated. • F A 2 DCU-autoMixed1: Query-time expert coefficient generation that used both visual and text experts

DCU at WikipediaMM 2009: Document expansion from Wikipedia abstracts

In this paper, we describe our participation in the WikipediaMM task at CLEF 2009. Our main efforts concern the expansion of the image metadata from the Wikipedia abstracts collection DBpedia. Since the metadata is short for retrieval by query words, we decided to expand the metadata using a typical query expansion method. In our experiments, we use the Rocchio algorithm for document expansion. Our best run is in the 26th rank of all 57 runs which is under our expectation, and we think that the main reason is that our document expansion method uses all the words from the metadata documents which contain words which are unrelated to the content of the images. Compared with our text retrieval baseline, our best document expansion run improves MAP by 11.17%. As one of our conclusions, we think that the document expansion can play an effective factor in the image metadata retrieval task. Our content-based image retrieval uses the same approach as in our participation in ImageCLEF 2008

K-Space at TRECVid 2007

In this paper we describe K-Space participation in TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance. The first of the two systems was a ‘shot’ based interface, where the results from a query were presented as a ranked list of shots. The second interface was ‘broadcast’ based, where results were presented as a ranked list of broadcasts. Both systems made use of the outputs of our high-level feature submission as well as low-level visual features

TRECVid 2005 experiments at Dublin City University

In this paper we describe our experiments in the automatic and interactive search tasks and the BBC rushes pilot task of TRECVid 2005. Our approach this year is somewhat different than previous submissions in that we have implemented a multi-user search system using a DiamondTouch tabletop device from Mitsubishi Electric Research Labs (MERL).We developed two versions of oursystem one with emphasis on efficient completion of the search task (Físchlár-DT Efficiency) and the other with more emphasis on increasing awareness among searchers (Físchlár-DT Awareness). We supplemented these runs with a further two runs one for each of the two systems, in which we augmented the initial results with results from an automatic run. In addition to these interactive submissions we also submitted three fully automatic runs. We also took part in the BBC rushes pilot task where we indexed the video by semi-automatic segmentation of objects appearing in the video and our search/browsing system allows full keyframe and/or object-based searching. In the interactive search experiments we found that the awareness system outperformed the efficiency system. We also found that supplementing the interactive results with results of an automatic run improves both the Mean Average Precision and Recall values for both system variants. Our results suggest that providing awareness cues in a collaborative search setting improves retrieval performance. We also learned that multi-user searching is a viable alternative to the traditional single searcher paradigm, provided the system is designed to effectively support collaboration

K-Space at TRECVid 2008

In this paper we describe K-Space’s participation in TRECVid 2008 in the interactive search task. For 2008 the K-Space group performed one of the largest interactive video information retrieval experiments conducted in a laboratory setting. We had three institutions participating in a multi-site multi-system experiment. In total 36 users participated, 12 each from Dublin City University (DCU, Ireland), University of Glasgow (GU, Scotland) and Centrum Wiskunde & Informatica (CWI, the Netherlands). Three user interfaces were developed, two from DCU which were also used in 2007 as well as an interface from GU. All interfaces leveraged the same search service. Using a latin squares arrangement, each user conducted 12 topics, leading in total to 6 runs per site, 18 in total. We officially submitted for evaluation 3 of these runs to NIST with an additional expert run using a 4th system. Our submitted runs performed around the median. In this paper we will present an overview of the search system utilized, the experimental setup and a preliminary analysis of our results

Diversity in image retrieval: DCU at ImageCLEFPhoto 2008

DCU participated in the ImageCLEF 2008 photo retrieval task, submitting runs for both the English and Random language annotation conditions. Our approaches used text-based and image-based retrieval approaches to give baseline retrieval runs, with the highest-ranked images from these baseline runs clustered using K-Means clustering of the text annotations. Finally, each cluster was represented by its most relevant image and these images were ranked for the nal submission. For random annotation language runs, we used TextCat1 to identify German annotation documents, which were then translated into English using Systran Version:3.0 Machine Translator. We also compared results from these translated runs with untranslated runs. Our results showed that, as expected, runs that combine image and text outperform text alone and image alone. Our baseline image+text runs (i.e. without clustering) give our best MAP score, and these runs also outperformed the mean and median ImageCLEFPhoto submissions for CR@20. Clustering approaches consistently gave a large improvement in CR@20 over the baseline, unclustered results. Pseudo relevance feedback consistently improved MAP while also consistently decreasing CR@20. We also found that the performance of untranslated random runs was quite close to that of translated random runs for CR@20, indicating that we could achieve similar diversity in our results without translating the documents

Experiments in terabyte searching, genomic retrieval and novelty detection for TREC 2004

In TREC2004, Dublin City University took part in three tracks, Terabyte (in collaboration with University College Dublin), Genomic and Novelty. In this paper we will discuss each track separately and present separate conclusions from this work. In addition, we present a general description of a text retrieval engine that we have developed in the last year to support our experiments into large scale, distributed information retrieval, which underlies all of the track experiments described in this document

Contour ripping: A tillage strategy to improve water infiltration into frozen soil

Practices that combine stubble management for snow catch and contour-ripping for snowmelt infiltration have potential to increase water infiltration and soil water storage. Our objective was to investigate sod lapping to improve water infiltration into frozen soil. Infiltration studies on frozen soil were conducted at sites near Pendleton, Oregon (silt loam soil), and Culbertson, Montana (sandy loam soil). Ripping was performed with a single chisel or parabolic subsoiling shank at 6- to 8-m intervals on the contour to a depth of 0.2 to 0.3 m. Final infiltration rate on the sandy loam averaged 11 mm h-1 on the rip treatment and 1 mm h-1 on the no-rip treatment even when the soil was frozen deeper than 0.6 m. On the silt loam soils, when the average depth of frozen soil was 0.14 m, average final infiltration rate was 28 mm h-1 on the rip treatment and 2 mm h-1 on the no-rip treatment. There were no treatment differences on the silt loam when the soil was frozen 0.35 in. Soil condition at the time of ripping determined the effectiveness of tillage to improve water infiltration; there was little benefit from ripping a dry pulverized soil because loose soil flowed into the rip and obliterated the rip path. Desirable macropore structure on loose soil was achieved by deferring ripping until the soil was frozen. Infiltration measurements show that soil ripping has potential to increase water infiltration and consequently decrease water runoff, and if used in conjunction with stubble management to maximize snow trapping, may increase overwinter soil water storage