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Image Search

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

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  1. Image Search Presented by: Samantha Mahindrakar Diti Gandhi

  2. 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 • Decreases in costs for storage capacity and processing

  3. 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

  4. 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’.

  5. Color • Color Characteristics of images • Selection of Color Space is important in CBIR • Examples  RGB, CMY • Averaging colors for regions • Histograms • Vectors

  6. Texture • 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

  7. Shape • 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.

  8. 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.

  9. Querying • 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.

  10. 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.

  11. Reducing semantic gap • Different techniques are used to reduce the semantic gaps • Object ontology • Machine learning • ALIPR • Relevant feedback • Semantic template • WWW image retrieval

  12. 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

  13. Relation to the architecture of Information Retrieval Systems Documents Query Representation Function Representation Function Query Representation Document Representation Index Comparison Function Hits

  14. 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

  15. 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

  16. Thank You !! Questions are welcome….

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