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Bindhu. N, Bala Murugan. C / International Journal of Engineering Research and Applications
                   (IJERA)         ISSN: 2248-9622        www.ijera.com
                       Vol. 3, Issue 2, March -April 2013, pp.416-419
 An Adaptive Novel Approach for Detection of Text and Caption
                         in Videos
                                  Bindhu. N1, Bala Murugan. C2
     1
      PG Scholar, Department of Computer Science and Engineering, Vel Tech Dr.RR & Dr.SR Technical
                                                 University,
                                 #42 Avadi-Vel Tech Road, Avadi, Chennai-62
   2
     Asst. Professor, Department of Computer Science and Engineering, Vel Tech Dr.RR & Dr.SR Technical
                           University, #42 Avadi-Vel Tech Road, Avadi, Chennai-62


ABSTRACT
         The video image spitted into number of           A straightforward video analysis is shot boundary
frames, each frame maintains the text. Then the           detection. Boundaries are typically found by
Image is converted into Gray Scale to avoid the           computing an image-based distance adjacent (or
text color variation. A single value is                   regularly) frames of the video and noting when this
corresponding to gray value and detecting the             distance exceeds a certain threshold. The distance
edge. Detecting the edge process is the boundary          between frames can be based on statistical properties
between two regions with relatively distinct gray-        of pixels, histogram differences [8], or motion
level properties. One is the horizontal direction of      detection [11]
the image. Another is the vertical direction of the                Other forms of scientific data including
image. The features to describe text regions are          Time Series, Medical Images( such as MRI, CT, and
area, saturation, orientation, aspect ratio and           PET ) and Seismic Data, have arching and search
position. Then convert into binary image. The             requirements similar to that for Satellite imagery
comer detected we can using the new techniques            [10].
for neural network. To apply the Harris corner
algorithm to implementing the Feature                              Content Based Retrieval can be used more
description by selecting the text area. The               effective by organizing the data. Cataloging is
Orientation is defined as the angle (ranging from         performed to help this task achieved. It is performed
to -90 degree to 90 degree) between the x-axis and        by relying on minimal human input [4]. In order to
the major axis of the ellipse that has the same           simplify and speed up this process, human input in
second-moments as the region. The input image is          Video content can be used as index for Video
given equalization based extracting the text. After       Browsing, this can include visual material that helps
finding out the text, it is checked with the              browsing, such as Key Frames, Story Board, Audio
database. If tracked image is matched with the            Clips [4], while Texture, Color Histogram can help
existing database. From that we can identify the          in image retrieval.
text of the particular image. Next input image is
spited into frames to extract text for sequences,         Design Challenges and search Strategies
and compare to previous text. Finally convert                      Content Based Retrieval of Images and
into voice.                                               Video Databases involve comparing a query object
                                                          with the objects stored in the data repository. The
Key words: Edge detection, text, video retrieval          search is usually based on similarity rather than on
                                                          exact match, and the retrieved results are then
Introduction                                              ranked according to a similarity index [6, 13].
         Efficient Management of large collection of
data in multimedia like Video, Satellite Imagery, are              The nature of video content as in a indoor
important issues that needs great attention and           video with a single speaker user verses an outdoor
research work, as the amount of data collected by         sports video impacts the retrieval [4].
Digital Libraries as Video, Satellite Imagery, tend to
grow exponentially, in order to reuse this data                      Video segmentation is a commonly used as
effectively, we need to organize this data such that it   a first step to automatically analyze content [4]. Shot
helps provide an effective access. Archival of Video      Boundary Detection Algorithms [1] are used to
clips to enable reuse is a time consuming, tedious        partition the video into elemental units called Shots.
and inefficient process [4].                              “Shot” is a fundamental unit of processing
         In Video images, index corresponds to an         (analyzing, indexing representation) of video. Shots
event occurred in video image, On the other hand [2]      are annotated with text and Keywords one or more
table of contents corresponds to a hierarchical           frames are extracted and treated as till images to
structure of topic.                                       apply visual search technology [4].



                                                                                                 416 | P a g e
Bindhu. N, Bala Murugan. C / International Journal of Engineering Research and Applications
                   (IJERA)         ISSN: 2248-9622        www.ijera.com
                       Vol. 3, Issue 2, March -April 2013, pp.416-419
Presentation of video. Shots are annotated with text     semantic network is to allow the generalization of
and Keywords one or more frames are extracted and        retrieval at the semantic level [10].
treated as till images to apply visual search
technology [4].

Data Organization and Content Based
Retrieval
          Significant images and words form a
paragraph are extracted to produce a short summary
of the Video. The cataloger supports time based and
content based key frame simplifying text based
annotation and metadata.
          Objects extracted at image ingestion time
can be indexed much more efficiently. However, it is
usually very difficult to anticipate all the types of
objects in which a user might be, and thus systems
allowing only search based on pre-extracted objects
are severely limiting. On the other hand, recognizing
objects entirely at query time will limit the
scalability of a system, due to the high expense of
such computing [10].
          These problems were alleviated by object-
oriented framework which allows flexible
composition of queries relying on both types of
objects. Within this frameworks, objects can be
specified at multiple abstraction levels namely, the
raw data level, the feature level, and the semantic
level as shown in figure:1 [10].                         Figure 1: Abstraction levels of an image.
          Raw Data: At the lowest abstraction level,
                                                         Data Organization and Retrieval
objects are simply aggregations of raw pixels from
                                                         The techniques to syntactically organize the video
the image. Comparison between objects or regions is
                                                         data are summarized as follows
done pixel-by-pixel Comparison at the pixel level is
very specific, and is therefore only used when a
                                                         1. Cut detection: The difference between consecutive
relatively precise match is required [10].
                                                            frames is computed based on a histogram method
                                                            and if it is greater than some threshold, it is
          Feature: The next higher abstraction level
                                                            regarded as a cut point.
for representing images is the feature level. An
                                                           The shot is extracted as the section ended at the cut
image feature is a distinguishing primitive
                                                            points [2].
characteristics or attribute of an image. Some
features such as luminance, shape descriptor, and
                                                         2. Extraction of common scene: Common scenes
gray scale texture are natural since they correspond
                                                           included in long video data give information about
to visual appearance of an image.
                                                           the repetition [2].
          Semantic: This is the highest abstraction
level at which a content-based search can be
                                                         3. Extraction of camera works: Camera work
performed. An object-oriented definition of a
                                                         indicates the intention of a cameraman or director.
semantic object also involves prescribing a set of
                                                         Therefore it is possible to segment the video data in
pertinent features or pixels as well as a method (such
                                                         terms of homogeneous work within a shot. The
as a classification algorithm with the appropriate
                                                         camera work can be extracted by a projection
training data). For satellite images, examples of
                                                         method or Affined parameters[2].
semantic objects include the type of land cover for a
specific area such as water, forest, or urban. For
                                                                  Let us now have a look at the scene change
medical images, examples include the type of organ
                                                         detection in video and analyze using the “Cut
such as liver, stomach, or colon. A semantic network
                                                         measure” and “dissolve measure” which were
can be constructed which groups similar semantic
                                                         discussed by Changick Kim in “Automated Shot
terms into categories. For example, pine and maple
                                                         Change Detection” CIDIL, Digital Video Library
are grouped into trees, rose and sunflower are
                                                         Project.
grouped into flowers, corn and wheat are grouped
into crops, etc. The purpose of constructing such a




                                                                                                417 | P a g e
Bindhu. N, Bala Murugan. C / International Journal of Engineering Research and Applications
                   (IJERA)         ISSN: 2248-9622        www.ijera.com
                       Vol. 3, Issue 2, March -April 2013, pp.416-419
“Cut”: In editing, an immediate switch from one                     Frame # 52          Frame #
image to anther, without the aid of transitions such
as dissolves or wipe.

“Dissolve”: A video transition in which the existing
image is partially or totally replaced by
superimposing another image, one image fades in as
the other fades out.

Here are the Frames taken from a Video Clip to          60                                           faden)
study the Cut and Dissolve measures.




                                                       Cut Frame : # 90        and       #91
Frame #1




Frame # 35,                 Frame #45,                 Cut frame: Frame # 928     and    # 929.

                                                       Conclusion
                                                                 Content Based video retrieval and Scene
                                                       change detection continue to pose challenging
                                                       problems, approaches that integrate different
                                                       indexing techniques like cataloging, Organization of
                                                       data that produce automatic table of contents, indices
                                                       and defining better ways to analyze the cut and
                                                       dissolve measures, Analyzing images at various
                                                       levels of abstractions and finding methods which
                                                       improve recognizing the objects entirely at query
                                                       time remain as promising avenues to be explored.
                                                       Summarizing video is a challenging user interface
                                                       problem. Thus proper organization and making use
                                                       of metadata efficiently can improve the performance
                                                       of Content Based Retrieval, which can be Further
                                                       applied to Educational Technical and Medical fields.




                                                                                               418 | P a g e
Bindhu. N, Bala Murugan. C / International Journal of Engineering Research and Applications
                   (IJERA)         ISSN: 2248-9622        www.ijera.com
                       Vol. 3, Issue 2, March -April 2013, pp.416-419
References:
 [1]   Arman, F., Depommier, R., Hsu, A., and
       Chiu, M.Y. Content based browsing of
       video sequences, in ACM Multimedia 94
       pp.97-103, Aug. 1994
 [2]   Ariki Yauso, Organization and Retrieval of
       Continues media, ACM Multimedia 2000,
       NOV 2000.
 [3]   C. Kim, Automated Shot change Detection,
       CIDIL, Digital Video Library Project.
 [4]   D. Ponceleon, S. Srinivasan, A. Amir
       D.Petkovic, Key to Effective Video
       Retrieval: Effective Cataloging and
       Browsing, ACM Multimedia 98, Sep 1998.
 [5]   E. Deardroff, T.D.C Little, J.D, Marshall
       and Venketasj, Video Scene Decomposition
       with the Motion Picture Paser, Proc, SPIE,
       vol. 2187, Digital Video Compression on
       Personal Computers: Algorithms and
       Technologies, Feb 1994, pp 44-55.
 [6]   F. Arman, A. Hsu, M. Y. Chiu, Feature
       Management of large video Database,
       Proc. SPIE vol 1908, 1993, pp 271-298.
 [7]   J. M. Gauch, S. Gauch, Scene Change
       Detection in Video Images, Design Lab,
       Digital Video Library System.
 [8]   J. Foote, J. Boreczky, A. Girensohn, and
       Lynn Wilcox,        An Intelligent Media
       Browser using Automatic Multimodal
       Analysis, ACM Multimedia 98, Sep 1998.
 [9]   L.      Rutledge,     Lhardman,       J.van
       Ossenburggen, D.C.A Bulterman, CWI,
       Amsterdam, Vrije, University, Amsterdam,
       Netherlands Structural Distinction between
       Hypermedia Storage and Presentation in
       ACM Multimedia 98, Sep 1998.
 [10] L. D. Bergman, V. Castelli, C. Li,
       Progressive Content Based Retrieval from
       Satellite Image Archives, D-Lib Magazine,
       Oct 97.
 [11] Shahrary, B., Scene change Detection and
       Content Based Sampling Video Sequences,
       in Digital Video Compression: Algorithms
       and Technologies, Rodriguez, Safranek,
       Delp, Eds., Proc APIE 2419, Feb, pp 2-13.
  [12] S.W. Smoliar and H. Zhang , Content based
       Video indexing and Retrieval, IEEE
       Multimedia, vol 1, no. 2, Summer 1994, pp
       62-72.




                                                                               419 | P a g e

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Bn32416419

  • 1. Bindhu. N, Bala Murugan. C / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.416-419 An Adaptive Novel Approach for Detection of Text and Caption in Videos Bindhu. N1, Bala Murugan. C2 1 PG Scholar, Department of Computer Science and Engineering, Vel Tech Dr.RR & Dr.SR Technical University, #42 Avadi-Vel Tech Road, Avadi, Chennai-62 2 Asst. Professor, Department of Computer Science and Engineering, Vel Tech Dr.RR & Dr.SR Technical University, #42 Avadi-Vel Tech Road, Avadi, Chennai-62 ABSTRACT The video image spitted into number of A straightforward video analysis is shot boundary frames, each frame maintains the text. Then the detection. Boundaries are typically found by Image is converted into Gray Scale to avoid the computing an image-based distance adjacent (or text color variation. A single value is regularly) frames of the video and noting when this corresponding to gray value and detecting the distance exceeds a certain threshold. The distance edge. Detecting the edge process is the boundary between frames can be based on statistical properties between two regions with relatively distinct gray- of pixels, histogram differences [8], or motion level properties. One is the horizontal direction of detection [11] the image. Another is the vertical direction of the Other forms of scientific data including image. The features to describe text regions are Time Series, Medical Images( such as MRI, CT, and area, saturation, orientation, aspect ratio and PET ) and Seismic Data, have arching and search position. Then convert into binary image. The requirements similar to that for Satellite imagery comer detected we can using the new techniques [10]. for neural network. To apply the Harris corner algorithm to implementing the Feature Content Based Retrieval can be used more description by selecting the text area. The effective by organizing the data. Cataloging is Orientation is defined as the angle (ranging from performed to help this task achieved. It is performed to -90 degree to 90 degree) between the x-axis and by relying on minimal human input [4]. In order to the major axis of the ellipse that has the same simplify and speed up this process, human input in second-moments as the region. The input image is Video content can be used as index for Video given equalization based extracting the text. After Browsing, this can include visual material that helps finding out the text, it is checked with the browsing, such as Key Frames, Story Board, Audio database. If tracked image is matched with the Clips [4], while Texture, Color Histogram can help existing database. From that we can identify the in image retrieval. text of the particular image. Next input image is spited into frames to extract text for sequences, Design Challenges and search Strategies and compare to previous text. Finally convert Content Based Retrieval of Images and into voice. Video Databases involve comparing a query object with the objects stored in the data repository. The Key words: Edge detection, text, video retrieval search is usually based on similarity rather than on exact match, and the retrieved results are then Introduction ranked according to a similarity index [6, 13]. Efficient Management of large collection of data in multimedia like Video, Satellite Imagery, are The nature of video content as in a indoor important issues that needs great attention and video with a single speaker user verses an outdoor research work, as the amount of data collected by sports video impacts the retrieval [4]. Digital Libraries as Video, Satellite Imagery, tend to grow exponentially, in order to reuse this data Video segmentation is a commonly used as effectively, we need to organize this data such that it a first step to automatically analyze content [4]. Shot helps provide an effective access. Archival of Video Boundary Detection Algorithms [1] are used to clips to enable reuse is a time consuming, tedious partition the video into elemental units called Shots. and inefficient process [4]. “Shot” is a fundamental unit of processing In Video images, index corresponds to an (analyzing, indexing representation) of video. Shots event occurred in video image, On the other hand [2] are annotated with text and Keywords one or more table of contents corresponds to a hierarchical frames are extracted and treated as till images to structure of topic. apply visual search technology [4]. 416 | P a g e
  • 2. Bindhu. N, Bala Murugan. C / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.416-419 Presentation of video. Shots are annotated with text semantic network is to allow the generalization of and Keywords one or more frames are extracted and retrieval at the semantic level [10]. treated as till images to apply visual search technology [4]. Data Organization and Content Based Retrieval Significant images and words form a paragraph are extracted to produce a short summary of the Video. The cataloger supports time based and content based key frame simplifying text based annotation and metadata. Objects extracted at image ingestion time can be indexed much more efficiently. However, it is usually very difficult to anticipate all the types of objects in which a user might be, and thus systems allowing only search based on pre-extracted objects are severely limiting. On the other hand, recognizing objects entirely at query time will limit the scalability of a system, due to the high expense of such computing [10]. These problems were alleviated by object- oriented framework which allows flexible composition of queries relying on both types of objects. Within this frameworks, objects can be specified at multiple abstraction levels namely, the raw data level, the feature level, and the semantic level as shown in figure:1 [10]. Figure 1: Abstraction levels of an image. Raw Data: At the lowest abstraction level, Data Organization and Retrieval objects are simply aggregations of raw pixels from The techniques to syntactically organize the video the image. Comparison between objects or regions is data are summarized as follows done pixel-by-pixel Comparison at the pixel level is very specific, and is therefore only used when a 1. Cut detection: The difference between consecutive relatively precise match is required [10]. frames is computed based on a histogram method and if it is greater than some threshold, it is Feature: The next higher abstraction level regarded as a cut point. for representing images is the feature level. An The shot is extracted as the section ended at the cut image feature is a distinguishing primitive points [2]. characteristics or attribute of an image. Some features such as luminance, shape descriptor, and 2. Extraction of common scene: Common scenes gray scale texture are natural since they correspond included in long video data give information about to visual appearance of an image. the repetition [2]. Semantic: This is the highest abstraction level at which a content-based search can be 3. Extraction of camera works: Camera work performed. An object-oriented definition of a indicates the intention of a cameraman or director. semantic object also involves prescribing a set of Therefore it is possible to segment the video data in pertinent features or pixels as well as a method (such terms of homogeneous work within a shot. The as a classification algorithm with the appropriate camera work can be extracted by a projection training data). For satellite images, examples of method or Affined parameters[2]. semantic objects include the type of land cover for a specific area such as water, forest, or urban. For Let us now have a look at the scene change medical images, examples include the type of organ detection in video and analyze using the “Cut such as liver, stomach, or colon. A semantic network measure” and “dissolve measure” which were can be constructed which groups similar semantic discussed by Changick Kim in “Automated Shot terms into categories. For example, pine and maple Change Detection” CIDIL, Digital Video Library are grouped into trees, rose and sunflower are Project. grouped into flowers, corn and wheat are grouped into crops, etc. The purpose of constructing such a 417 | P a g e
  • 3. Bindhu. N, Bala Murugan. C / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.416-419 “Cut”: In editing, an immediate switch from one Frame # 52 Frame # image to anther, without the aid of transitions such as dissolves or wipe. “Dissolve”: A video transition in which the existing image is partially or totally replaced by superimposing another image, one image fades in as the other fades out. Here are the Frames taken from a Video Clip to 60 faden) study the Cut and Dissolve measures. Cut Frame : # 90 and #91 Frame #1 Frame # 35, Frame #45, Cut frame: Frame # 928 and # 929. Conclusion Content Based video retrieval and Scene change detection continue to pose challenging problems, approaches that integrate different indexing techniques like cataloging, Organization of data that produce automatic table of contents, indices and defining better ways to analyze the cut and dissolve measures, Analyzing images at various levels of abstractions and finding methods which improve recognizing the objects entirely at query time remain as promising avenues to be explored. Summarizing video is a challenging user interface problem. Thus proper organization and making use of metadata efficiently can improve the performance of Content Based Retrieval, which can be Further applied to Educational Technical and Medical fields. 418 | P a g e
  • 4. Bindhu. N, Bala Murugan. C / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.416-419 References: [1] Arman, F., Depommier, R., Hsu, A., and Chiu, M.Y. Content based browsing of video sequences, in ACM Multimedia 94 pp.97-103, Aug. 1994 [2] Ariki Yauso, Organization and Retrieval of Continues media, ACM Multimedia 2000, NOV 2000. [3] C. Kim, Automated Shot change Detection, CIDIL, Digital Video Library Project. [4] D. Ponceleon, S. Srinivasan, A. Amir D.Petkovic, Key to Effective Video Retrieval: Effective Cataloging and Browsing, ACM Multimedia 98, Sep 1998. [5] E. Deardroff, T.D.C Little, J.D, Marshall and Venketasj, Video Scene Decomposition with the Motion Picture Paser, Proc, SPIE, vol. 2187, Digital Video Compression on Personal Computers: Algorithms and Technologies, Feb 1994, pp 44-55. [6] F. Arman, A. Hsu, M. Y. Chiu, Feature Management of large video Database, Proc. SPIE vol 1908, 1993, pp 271-298. [7] J. M. Gauch, S. Gauch, Scene Change Detection in Video Images, Design Lab, Digital Video Library System. [8] J. Foote, J. Boreczky, A. Girensohn, and Lynn Wilcox, An Intelligent Media Browser using Automatic Multimodal Analysis, ACM Multimedia 98, Sep 1998. [9] L. Rutledge, Lhardman, J.van Ossenburggen, D.C.A Bulterman, CWI, Amsterdam, Vrije, University, Amsterdam, Netherlands Structural Distinction between Hypermedia Storage and Presentation in ACM Multimedia 98, Sep 1998. [10] L. D. Bergman, V. Castelli, C. Li, Progressive Content Based Retrieval from Satellite Image Archives, D-Lib Magazine, Oct 97. [11] Shahrary, B., Scene change Detection and Content Based Sampling Video Sequences, in Digital Video Compression: Algorithms and Technologies, Rodriguez, Safranek, Delp, Eds., Proc APIE 2419, Feb, pp 2-13. [12] S.W. Smoliar and H. Zhang , Content based Video indexing and Retrieval, IEEE Multimedia, vol 1, no. 2, Summer 1994, pp 62-72. 419 | P a g e