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A Semantic Approach for Improving Scene Understanding

مقارنة دلالية لتحسين فهم المشهد

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 Publication date 2015
and research's language is العربية
 Created by Shamra Editor




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People live in various environments, although they can understand scenes around them with just a glance. To do this, they depend on their high ability to effectively process visual data and connect it to wide pre-knowledge about what they are expected to see. This is not the case for computers, which can’t reach high levels of scene understanding until now. Most researches treat scene understanding as a usual classification problem, where they have just to classify scenes in predefined limited categories (forest, city, garden). They normally used classification or machine learning algorithms, which limit their ability to understand scenes and reduces their chances to be used in a practical way because of a required training phase of these algorithms. Some researches try to make use of knowledge in Ontologies to reach a high level scene understanding, but these researches are still limited to specific domains only. In this thesis we are trying to understand scene images without any pre-knowledge about their domain. We will not treat this problem as a normal classification problem; however we will extract high level concepts from scene images. These concepts will not only represent objects in the scene, but they will also reflect the places and events in the scene. To do this, we develop a novel algorithm named SMHITS. It depends on a semantically rich common sense knowledge base to extract associated concepts with a primitive group of concepts. To use SMHITS in scene understanding, we also develop a system named ICES. Instead of using a classification or machine learning algorithm, ICES depends on a large dataset of images that is independent of any scene domain. Results show the superiority of SMHITS comparing to current ConceptNet associated concepts extraction algorithm, as it has higher precision and can take advantage of expansion of its knowledge base. Results also show that ICES output concepts are semantically rich.


Artificial intelligence review:
Research summary
تتناول هذه الأطروحة مشكلة فهم المشاهد من خلال تطوير نظام جديد يعتمد على استخراج المفاهيم الضمنية من الصور بدلاً من تصنيفها ضمن تصنيفات محددة مسبقاً. تعتمد الأطروحة على تطوير خوارزمية جديدة تسمى SMHITS التي تعتمد على شبكة معارف شائعة لاستخراج المفاهيم المرتبطة دلالياً بمجموعة من المفاهيم الأولية المستخرجة من الصور. يتكون النظام المقترح، المسمى ICES، من مرحلتين: الأولى تعتمد على قاعدة صور غير متخصصة لاستخراج المفاهيم الأولية، والثانية تعتمد على خوارزمية SMHITS لاستخراج المفاهيم الضمنية. أظهرت النتائج تفوق خوارزمية SMHITS على الخوارزميات الحالية من حيث الدقة والغنى الدلالي للمفاهيم المستخرجة.
Critical review
على الرغم من أن الأطروحة تقدم حلاً مبتكراً لمشكلة فهم المشاهد، إلا أن هناك بعض النقاط التي يمكن تحسينها. أولاً، تعتمد الأطروحة بشكل كبير على قاعدة الصور المستخدمة، مما قد يحد من تطبيق النظام في مجالات أخرى تحتاج إلى قواعد صور مختلفة. ثانياً، لا تزال الخوارزمية تعتمد على شبكة معارف شائعة قد تحتوي على بعض الأخطاء أو التناقضات في العلاقات الدلالية. ثالثاً، يمكن تحسين النظام من خلال دمج تقنيات تعلم الآلة الحديثة مثل التعلم العميق لتحسين دقة استخراج المفاهيم الضمنية.
Questions related to the research
  1. ما هي الخوارزمية الجديدة التي تم تطويرها في هذه الأطروحة؟

    الخوارزمية الجديدة التي تم تطويرها تسمى SMHITS، وهي تعتمد على شبكة معارف شائعة لاستخراج المفاهيم المرتبطة دلالياً بمجموعة من المفاهيم الأولية المستخرجة من الصور.

  2. ما هي المراحل التي يتكون منها نظام ICES؟

    يتكون نظام ICES من مرحلتين: الأولى تعتمد على قاعدة صور غير متخصصة لاستخراج المفاهيم الأولية، والثانية تعتمد على خوارزمية SMHITS لاستخراج المفاهيم الضمنية.

  3. ما هي النتائج التي أظهرتها خوارزمية SMHITS مقارنة بالخوارزميات الحالية؟

    أظهرت النتائج تفوق خوارزمية SMHITS على الخوارزميات الحالية من حيث الدقة والغنى الدلالي للمفاهيم المستخرجة.

  4. ما هي النقاط التي يمكن تحسينها في الأطروحة؟

    يمكن تحسين الأطروحة من خلال تقليل الاعتماد على قاعدة الصور المستخدمة، تحسين دقة شبكة المعارف الشائعة، ودمج تقنيات تعلم الآلة الحديثة مثل التعلم العميق.


References used
L. Shapiro and G. C. Stockman, Computer Vision: Prentice Hall, 2001
. R. Davies, Machine Vision: Theory, Algorithms, Practicalities: Morgan Kaufmann Publishers Inc., 2004
. Szeliski, Computer Vision: Algorithms and Applications: Springer-Verlag New York, Inc., 2010.
B. Jiihne and H. Hauflecker, Computer Vision and Applications: A Guide for Students and Practitioners: Academic Press, San Diego, California, 2000.
N. Pears, Y. Liu, and P. Bunting, 3D Imaging, Analysis and Applications :Springer, 2012
A. Oliva, "Scene Perception," in the New Visual Neurosciences, E. J. S. Werner and L. M. Chalupa, Eds., ed: MIT Press, 2012.
A. Oliva, "Visual Scene Perception," Massachusetts Institute of Technology 2009.
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