Content-based video indexing and retrieval

- PHẠM QUANG HẢI CONTENT-BASED VIDEO INDEXING AND RETRIEVAL LUẬN VĂN THẠC SĨ KỸ THUẬT XỬ LÝ THÔNG TIN Và TRUYỀN THÔNG Hà Nội – 2005 BỘ GIÁO DỤC VÀ ĐÀO TẠO TRƯỜNG ĐẠI HỌC BÁCH KHOA HÀ NỘI.
- PHẠM QUANG HẢI CONTENT-BASED VIDEO INDEXING AND RETRIEVAL LUẬN VĂN THẠC SĨ KỸ THUẬT XỬ LÝ THÔNG TIN Và TRUYỀN THÔNG NGƯỜI HƯỚNG DẪN KHOA HỌC: Alain Boucher Hà Nội - 2005 Thesis for Degree of Master - Content-based video indexing and retrieval Pham Quang Hai i Abstract Video indexing and retrieval is an important element of any large multimedia database.
- Continue to use corner-based motion combining with histogram features, by measuring distance of how far the motion move, key frames is selected, and it is ready for indexing and retrieval application.
- It is an overview of video indexing and retrieval system: what they did, what they are doing and how they will do.
- Thesis for Degree of Master - Content-based video indexing and retrieval Pham Quang Hai ii Acknowledgements This work is a part in Multimedia Information Communication Application (MICA) research central.
- I would like to thank to directors in MICA: Mr.
- Thesis for Degree of Master - Content-based video indexing and retrieval Pham Quang Hai iii Contents Abstract.
- Content-based Video Indexing and Retrieval (CBVIR.
- Content-based video indexing and retrieval system.
- Video sequence structure.
- 44 Chapter III Video Indexing by Camera motions using Corner-based motion vector.
- Video and Image Parsing in MICA.
- 47 Thesis for Degree of Master - Content-based video indexing and retrieval Pham Quang Hai iv III.3.2.
- Video Indexing.
- 81 Thesis for Degree of Master - Content-based video indexing and retrieval Pham Quang Hai v List of abbreviations CD: Compact Disk DVD: Digital Versatile Disk MPEG: Moving Pictures Experts Group CBVIR: Content Based Video Indexing and Retrieval CBIR: Content Based Indexing and Retrieval IEC: International Electro-technical Commission DCT: Discrete Cosine Transform JPEG: Joint Photographic Experts Group IDC: Inverse Discrete Cosine Transform GOP: Group of Pictures Thesis for Degree of Master - Content-based video indexing and retrieval Pham Quang Hai vi List of figures Figure I-1 Position of video system in MICA central.
- 2 Figure II-1 Two consecutive frames from video sequence.
- 5 Figure II-2 Motion Compensation from MPEG video stream.
- 6 Figure II-3 Block diagram of MPEG video encoder.
- 6 Figure II-4 Video Hierarchical Structure.
- 8 Figure II-5 Common directions of moving video camera.
- 11 Figure II-6 Common rotation and zoom of stationary video camera.
- 12 Figure II-7 CVBIR common system diagram.
- 13 Figure II-8 Classification of video modeling technique.
- 14 Figure II-9 Process diagram of CBVIR system.
- 15 Figure II-10 RGB color space (picture source [SEMMIX.
- 19 Figure II-11 HSV color space (picture source [SEMMIX.
- 19 Figure II-12 Tamura features and their values (a) Coarseness (b) Contrast (c) Directionality.
- 20 Figure II-13 Effect of Gabor Filter to image results.
- 20 Figure II-14 Shot Transitions (a) cut (b) fade-in (c) fade-out (d) dissolve (e) wipe.
- 23 Figure II-15 Some transition effects for wipe (pictures taken from Pinnacle Software.
- 23 Figure II-16 Reduce the number of bits during calculate the histogram.
- 26 Figure II-17 Cut (a) and (Fade/Dissolve) from frame difference.
- 27 Figure II-18 Twin Comparison (picture taken from [II.4 5.
- 28 Figure II-19: Head tracking for determine trajectories.
- 31 Figure II-20: The 2D motion trajectory (third direction is frame time line.
- 31 Figure II-21 Optical flow (a) two frame from video sequence (b) optical flow.
- 33 Figure II-22 Optical flow filed produced by pan and track, tilt and boom, zoom and dolly.
- 34 Figure II-23 Motion segmentation by optical flow.
- 36 Figure II-24 Local and Global Contextual Information.
- 42 Figure III-1 Relation between R and eigenvalues.
- 48 Figure III-2 Harris Corner in image with different given corner number.
- 49 Figure III-3 (a)Two frames extracted while camera pan right (b) correspond points results, drew lines in frame#760.
- 51 Figure III-4 Results from no shot transition.
- 54 Figure III-5 Results from shot cut transition.
- 55 Figure III-6 Results from dissolve.
- 57 Figure III-7 Correspondent points matching numbers in one video sequence.
- 60 Figure III-8 Two frames from two shots but similar.
- 62 Figure III-9 Correspondent points in video sequence 3.
- 62 Figure III-10 Frame sequence from video sequence 3.
- 63 Figure III-11 Keep motion vectors by given threshold for magnitudes.
- 65 Figure III-12 8 used directions for standardizing vector directions.
- 66 Figure III-13 Some consecutive frames from pan right shot.
- 66 Thesis for Degree of Master - Content-based video indexing and retrieval Pham Quang Hai vii Figure III-14 Video frame from video sequence 1.
- 72 Figure III-15 Video frame from video sequence 2.
- 72 Figure III-16 Key frame selection from video mosaic.
- 74 Figure III-17 Key frames is selected from motion graph.
- 75 Figure III-18 Complicated motion graph from video.
- 75 Figure III-19 cases of vector graph.
- 76 Figure III-20 Results of key frame selection.
- 76 Figure III-21 Hierarchical indexing for CBVIR system.
- 78 Thesis for Degree of Master - Content-based video indexing and retrieval Pham Quang Hai viii List of tables Table 1 Test data used for shot cut algorithm.
- 70 Table 6 Video sequence for global motion.
- 71 Table 7 Three table of motion vectors for video sequence 1, 2 and 6.
- 72 Table 8 Global motion from video sequence 3.
- 73 Thesis for Degree of Master - Content-based video indexing and retrieval Pham Quang Hai 1 Chapter I Introduction I.1.
- Content-based Video Indexing and Retrieval (CBVIR) Nowadays, video material increases very fast and video data became very popular and numerous.
- That reasons made the information of video became hugeness, achieve difficulty, getting messy when people tried to browse his need video information.
- Position of video system is showed in Figure I-1.
- Thesis for Degree of Master - Content-based video indexing and retrieval Pham Quang Hai 2 International ProjectsMICA central, VietnamSpeech ProcessingImage ProcessingVideoProcessingCommunicationMultimediaSystemSmart roomNETWORKother centrals Figure I-1 Position of video system in MICA central I.3.
- This chapter provides basic information, characteristic of video and a general CBVIR system.
- Thesis for Degree of Master - Content-based video indexing and retrieval Pham Quang Hai 3 Chapter II Background II.1.
- Video Formats There are numbers of video format to use in CBVIR systems.
- An advantage of MPEG format is reducing the size of video file into small that makes a lot of video processing system became available.
- While encoding MPEG video stream, they used “two-steps” to compress video: Once for spatial compress and once for motion compress (motion compensation).
- The requirement of applications that used MPEG video can be played in anywhere: Digital Storage Media requires small size, good quality enough to process because of its cost, asymmetric applications requires the ability of subdivision for video delivery (e.g.
- Introduction to MPEG The Moving Pictures Experts Group abbreviated MPEG is part of the International Standards Organisation (ISO), and defines standards for digital video and digital audio.
- Thesis for Degree of Master - Content-based video indexing and retrieval Pham Quang Hai 4 Meanwhile the demands have risen and beside the CD the DVD needs to be supported as well as transmission equipment like satellites and networks.
- MPEG-2 Video Standard MPEG-2 video is an ISO/IEC standard that specifies the syntax and semantics of an enclosed video bitstream.
- The range of possibilities of the MPEG-2 standard is so wide that not all features of the standard are used for all applications [KEITH].
- MPEG-2 Encoding One of the most interest points of MPEG is reduce the size of video into smallest as it can.
- Imagine a scene where at first there is no Thesis for Degree of Master - Content-based video indexing and retrieval Pham Quang Hai 5 movement, and then an object moves across the picture.
- The first picture in the sequence contains all the information required until there is any movement, so there is no need to encode any of the information after the first picture until the movement occurs.
- Thereafter, all that needs to be encoded is the part of the picture that contains movement.
- The rest of the scene is not affected by the moving object because it is still the same as the first picture.
- The information obtained from this process is then used by motion compensated prediction to define the parts of the picture that can be discarded.
- Motion compensation can be description in Figure II-1.
- Figure II-1 Two consecutive frames from video sequence Like what we have seen at two consecutive frames, the man on the right and the background are staying static, the man on the left is moving.
- All information we need to store is back ground, figure of the man on the right, and the motion figure of the man on the left.
- The compensation motion here is the next frame is create from the last frame plus the part which arisen from motion and subtract the part which overridden from Thesis for Degree of Master - Content-based video indexing and retrieval Pham Quang Hai 6 new part [CALIC].
- The compensation of motion we considered can be described in visual in Figure II-2.
- Figure II-2 Motion Compensation from MPEG video stream Block diagram of MPEG video encoder can be described in Figure II-3.
- DCT Q1 VLCIDC+MCPFrames Data MPEG video Figure II-3 Block diagram of MPEG video encoder Fist of all, frames data (raw data) is compressed by DCT (Discrete Cosine Transform) by divide a frame in to macroblocks and calculate block DC coefficient

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