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Image Search. Presented by: Samantha Mahindrakar Diti Gandhi. Introduction. Definition The process of retrieving and displaying relevant images based on user’s queries from a database Contributing Factors for Image Search Increase in the availability and demand of digital images

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image search

Image Search

Presented by:

Samantha Mahindrakar

Diti Gandhi

  • Definition
    • The process of retrieving and displaying relevant images based on user’s queries from a database
  • Contributing Factors for Image Search
    • Increase in the availability and demand of digital images
    • Decreases in costs for storage capacity and processing
text based image retrieval tbir
Text-Based Image Retrieval (TBIR)
  • Uses text descriptions to retrieve relevant images
  • Time, location, events, objects
  • Advantage – based on currently in use text retrieval system
  • Disadvantage – Inconsistency based on the variation of human interpretation
  • Google Image Search, AltaVista Image Search
content based image retrieval cbir
Content-Based Image Retrieval (CBIR)
  • Extracts images based on image content.
    • Level 1: Retrieval by primitive features such as color, texture, shape and spatial location.
    • Level 2: Retrieval of objects of given type. Example: find the picture of the flower.
    • Level 3: Retrieval of abstract attributes that involves high level reasoning. Example: ‘find picture of a baby smiling’.
  • Color Characteristics of images
  • Selection of Color Space is important in CBIR
      • Examples  RGB, CMY
  • Averaging colors for regions
  • Histograms
  • Vectors
  • Visual Pattern
  • Uniform intensity region of a simple shape that is repeated
  • Two Types of Analysis
    • Structural  texture elements are used for determining shapes and placement within a image
    • Statistical  used for fine texture
  • Involves detecting the border and boundary of objects.
  • This is done by edge detection algorithms.
  • They measure area, circularity, shape signature, curvature, eccentricity to determine the shape.
  • Different objects such as flowers, brain tumors etc require different algorithms. Hence shape preprocessing algorithms are application dependent.
spatial location
Spatial Location
  • This feature is used for region classification.
  • Spatial location are defined as ‘upper, bottom, top’ according to the location of the region in the image.
    • For example sea and sky could have similar color and texture features, but their spatial locations are different.
  • Simple Visual Feature Query: The user specifies values for feature.
  • Feature combination query: Specify values for combination of different features.
  • Localized feature query: The user indicates feature values and locations by placing regions on the canvas.
  • Query by example: The system provides a set of random images. The user selects an image closest to their query. The system then finds similar images.
  • Concept queries: User can specify concept such as laughing as a query.
semantic gap
Semantic Gap
  • Semantic gap is the difference between human perception of a concept and how it can be represented using machine level language.
    • Sky can represented as light blue (color), upper (spatial), uniform (texture).
  • The focus is now shifted from designing low- level image features to reducing the semantic gap between the visual features and richness of human semantics.
reducing semantic gap
Reducing semantic gap
  • Different techniques are used to reduce the semantic gaps
    • Object ontology
    • Machine learning
      • ALIPR
    • Relevant feedback
    • Semantic template
    • WWW image retrieval
ia and image search
IA and Image search
  • The problem that is inherent for information retrieval also applies to image retrieval i.e. semantic gap.
  • Evaluation Metrics
      • Recall  Percentage of all Relevant Images in the Search Database which are Retrieved
      • Precision Percentage of Retrieved Pictures that are Relevant to the Query

Relation to the architecture of Information Retrieval Systems







Query Representation

Document Representation





future implications
Future Implications
  • Continue to bridge the semantic gap
  • Descriptive Story Text to Image Retrieval
      • News agencies and Educational Story Illustrations
  • Connecting Emotion to Images for Retrieval
  • Using Image Retrieval to Preserve Art
  • Image Retrieval for People with Disability
      • Combine Sound with Search
  • Medical Applications
      • Improved Diagnosis
reference sources
Reference Sources
  • A Survey of content-based image retrieval with high-level semantics
  • Techniques and Systems for Image and Video Retrieval
  • Real-Time Computerized Annotation of Images (ALIPR)
  • SIMPLIcity: Semantics-Sensitive Integrated matching for Picture Libraries
  • Image Retrieval: Ideas, Influences, and Trends of the New Age
  • Content-based image retrieval: a comparison between query by example and image browsing map approaches
Thank You !!

Questions are welcome….