Distribution Rules for Array Database Queries

Non-trivial retrieval applications involve complex computations on large multi-dimensional datasets. These should, in principle, benefit from the use of relational database technology. However, expressing such problems in terms of relational queries is difficult and timeconsuming. Even more discouraging is the efficiency issue: query optimization strategies successful in classical relational domains may not suffice when applied to the multi-dimensional array domain. The RAM (Relational Array Mapping) system hides these difficulties by providing a transparent mapping between the scientific problem specification and the underlying database system. In addition, its optimizer is specifically tuned to exploit the characteristics of the array paradigm and to allow for automatic balanced work-load distribution. Using an example taken from the multimedia domain, this paper shows how a distributed realword application can be efficiently implemented, using the RAM system, without user intervention

Automatic optimization of array queries

Non-trivial scientific applications often involve complex computations on large multi-dimensional datasets. Using relational database technology for these datasets is cumbersome since expressing the computations in terms of relational queries is difficult and time-consuming. Moreover, query optimization strategies successful in classical relational domains may not suffice when applied to the multi-dimensional array domain. The RAM (Relational Array Mapping) system hides these issues by providing a transparent mapping between the scientific problem specification and the underlying database system. This paper focuses on the RAM query optimizer which is specifically tuned to exploit the characteristics of the array paradigm. We detail how an intermediate array-algebra and several equivalence rules are used to create efficient query plans and how, with minor extensions, the optimizer can automatically parallelize array operation

Presenting Visual Information to the User: Combining Computer Vision and Interface Design

In this work, we suggest better ways to present visual information (image databases) for browsing and retrieval. Thumbnails obtained from an image set give a good overview of its contents. Instead of simply downsampling images to obtain thumbnails, we first find salient regions (saliency map) using local statistical features of the image. We crop and downsample the images based on these saliency maps, and obtain better thumbnails. The suggested methods of finding salient regions are faster than existing methods while giving comparable results. Secondly, we have developed a Content Based Image Retrieval (CBIR) system to provide empirical evidence (by user study) that similarity based grouped and hierarchical placement of images is better than random placement. Using an effective shape based similarity measure we conclude that visual search is very useful in image retrieval systems. We conducted a field test to check the robustness of the system in varying photography conditions

Searching for repeated video sequences

In this paper, we propose a new method to search different instances of a video sequence inside a long video and/or video collection. The proposed method is robust to view point and illumination changes which may occur since the sequences are captured in different times with different cameras, and to the differences in the order and the number of frames in the sequences which may occur due to editing. The algorithm does not require any query to be given for searching, and finds all repeating video sequences inside a long video in a fully automatic way. First, the frames in a video are ranked according to their similarity on the distribution of salient points and colour values. Then, a tree based approach is used to seek for the repetitions of a video sequence if there is any. Results are provided on a full length feature movie, Run Lola Run and on commercials of TRECVID 2004 news video corpus. Copyright 2007 ACM

Report from the first international workshop on computer vision meets databases (CVDB 2004)

International audienceThis report summarizes the presentations and discussions of the First International Workshop on Computer Vision meets Databases, or CVDB 2004, which was held in Paris, France, on June 13, 2004. The workshop was co-located with the 2004 ACM SIGMOD/PODS conferences and was attended by forty-two participants from all over the world

Detection and tracking of repeated sequences in videos

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2007.Thesis (Master's) -- Bilkent University, 2007.Includes bibliographical references leaves 87-92.In this thesis, we propose a new method to search different instances of a video sequence inside a long video. The proposed method is robust to view point and illumination changes which may occur since the sequences are captured in different times with different cameras, and to the differences in the order and the number of frames in the sequences which may occur due to editing. The algorithm does not require any query to be given for searching, and finds all repeating video sequences inside a long video in a fully automatic way. First, the frames in a video are ranked according to their similarity on the distribution of salient points and colour values. Then, a tree based approach is used to seek for the repetitions of a video sequence if there is any. These repeating sequences are pruned for more accurate results in the last step. Results are provided on two full length feature movies, Run Lola Run and Groundhog Day, on commercials of TRECVID 2004 news video corpus and on dataset created for CIVR Copy Detection Showcase 2007. In these experiments, we obtain %93 precision values for CIVR2007 Copy Detection Showcase dataset and exceed %80 precision values for other sets.Can, TolgaM.S

3D forensic crime scene reconstruction involving immersive technology: A systematic literature review

Recreation of 3D crime scenes is critical for law enforcement in the investigation of serious crimes for criminal justice responses. This work presents a premier systematic literature review (SLR) that offers a structured, methodical, and rigorous approach to understanding the trend of research in 3D crime scene reconstruction as well as tools, technologies, methods, and techniques employed thereof in the last 17 years. Major credible scholarly database sources, Scopus, and Google Scholar, which index journals and conferences that are promoted by entities such as IEEE, ACM, Elsevier, and SpringerLink were explored as data sources. Of the initial 17, 912 papers that resulted from the first search string, 258 were found to be relevant to our research questions after implementing the inclusion and exclusion criteria

Semantic interpretation of events in lifelogging

The topic of this thesis is lifelogging, the automatic, passive recording of a person’s daily activities and in particular, on performing a semantic analysis and enrichment of lifelogged data. Our work centers on visual lifelogged data, such as taken from wearable cameras. Such wearable cameras generate an archive of a person’s day taken from a first-person viewpoint but one of the problems with this is the sheer volume of information that can be generated. In order to make this potentially very large volume of information more manageable, our analysis of this data is based on segmenting each day’s lifelog data into discrete and non-overlapping events corresponding to activities in the wearer’s day. To manage lifelog data at an event level, we define a set of concepts using an ontology which is appropriate to the wearer, applying automatic detection of concepts to these events and then semantically enriching each of the detected lifelog events making them an index into the events. Once this enrichment is complete we can use the lifelog to support semantic search for everyday media management, as a memory aid, or as part of medical analysis on the activities of daily living (ADL), and so on. In the thesis, we address the problem of how to select the concepts to be used for indexing events and we propose a semantic, density- based algorithm to cope with concept selection issues for lifelogging. We then apply activity detection to classify everyday activities by employing the selected concepts as high-level semantic features. Finally, the activity is modeled by multi-context representations and enriched by Semantic Web technologies. The thesis includes an experimental evaluation using real data from users and shows the performance of our algorithms in capturing the semantics of everyday concepts and their efficacy in activity recognition and semantic enrichment