Content-based video copy detection using multimodal analysis

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 67-76.Huge and increasing amount of videos broadcast through networks has raised the need of automatic video copy detection for copyright protection. Recent developments in multimedia technology introduced content-based copy detection (CBCD) as a new research field alternative to the watermarking approach for identification of video sequences. This thesis presents a multimodal framework for matching video sequences using a three-step approach: First, a high-level face detector identifies facial frames/shots in a video clip. Matching faces with extended body regions gives the flexibility to discriminate the same person (e.g., an anchor man or a political leader) in different events or scenes. In the second step, a spatiotemporal sequence matching technique is employed to match video clips/segments that are similar in terms of activity. Finally the non-facial shots are matched using low-level visual features. In addition, we utilize fuzzy logic approach for extracting color histogram to detect shot boundaries of heavily manipulated video clips. Methods for detecting noise, frame-droppings, picture-in-picture transformation windows, and extracting mask for still regions are also proposed and evaluated. The proposed method was tested on the query and reference dataset of CBCD task of TRECVID 2008. Our results were compared with the results of top-8 most successful techniques submitted to this task. Experimental results show that the proposed method performs better than most of the state-of-the-art techniques, in terms of both effectiveness and efficiency.Küçüktunç, OnurM.S

Fuzzy Color Histogram-based Video Segmentation

We present a fuzzy color histogram-based shot-boundary detection algorithm specialized for content based copy detection applications. The proposed method aims to detect both cuts and gradual transitions (fade, dissolve) effectively in videos where heavy transformations (such as cam-cording, insertions of patterns, strong re-encoding) occur. Along with the color histogram generated with the fuzzy linking method on L*a*b* color space, the system extracts a mask for still regions and the window of picture-in-picture transformation for each detected shot, which will be useful in a content-based copy detection system. Experimental results show that our method effectively detects shot boundaries and reduces false alarms as compared to the state-of-the-art shot-boundary detection algorithms

U˘gur Güdükbay,

We have recently developed a video database system, BilVideo, which provides integrated support for spatiotemporal, semantic, and low-level feature queries. 1 As a further development for this system, we present a natural language-based interface for query specification. This natural language processing (NLP)-based interface lets users formulate queries as sentences in English by using a partof-speech (POS) tagging algorithm. The system then groups the specified queries as object-appearance, spatial, and similarity-based object trajectory queries by using POS tagging information. It sends the queries constructed in the form of Prolog facts to the query processing engine, which interacts with both the knowledge base and object-relational database to respond to user queries that contain a combination of spatiotemporal, semantic, color, shape, and texture video queries. The query processor seamlessly integrates the intermediate query results returned from these two system components. The system sends the final results to Web clients. What motivates our work is the need for a convenient and flexible natural language-based interface to complement the text-based query interface and the visual query interface of the BilVideo system, because specification of spatial queries using text or visual interfaces is not very easy for novice users. (For examples of how others have attempted to handle these issues, see the “Related Work ” sidebar, next page.) Thus, we developed a natural language-based query interface that’s convenient and offers greater flexibility when specifying queries. The POS-based pattern-matching approach we use in identifying queries helps users specify queries without conforming to strict rules. This approach also lets us adjust our query interface easily as we add new query types to the BilVideo system

Fast Recommendation on Bibliographic Networks

Abstract—Graphs and matrices are widely used in algorithms for social network analyses. Since the number of interactions is much less than the possible number of interactions, the graphs and matrices used in the analyses are usually sparse. In this paper, we propose an efficient implementation of a sparsematrix computation which arises in our publicly available citation recommendation service called theadvisor. The recommendation algorithm uses a sparse matrix generated from the citation graph. We observed that the nonzero pattern of this matrix is highly irregular and the computation suffers from high number of cache misses. We propose techniques for storing the matrix in memory efficiently and reducing the number of cache misses. Experimental results show that our techniques are highly efficient on reducing the query processing time which is highly crucial for a web service. Index Terms—Citation recommendation; social network analysis; sparse matrices; hypergraphs; cache locality. I

Recommendation on Academic Networks using Direction Aware Citation Analysis,” arXiv preprint arXiv:1205.1143

The literature search has always been an important part of an academic research. It greatly helps to improve the quality of the research process and output, and increase the efficiency of the researchers in terms of their novel contribu-tion to science. As the number of published papers increases every year, a manual search becomes more exhaustive even with the help of today’s search engines since they are not specialized for this task. In academics, two relevant papers do not always have to share keywords, cite one another, or even be in the same field. Although a well-known paper is usually an easy pray in such a hunt, relevant papers using a different terminology, especially recent ones, are not obvious to the eye. In this work, we propose paper recommendation algo