1. Current Topics in Information Access: Image Retrieval Chad Carson EECS Department UC Berkeley SIMS 296a-3 December 2, 1998

2. Outline Background –What do collections look like? –What do users want? Sample image retrieval systems –Piction: retrieve photos (of people) based on captions –WebSeek: Web search based (mostly) on text –QBIC: retrieve general photos using content, text –Blobworld: rely exclusively on image content Discussion

3. Image collections are diverse

4. What are the tasks? Large, diverse image collections are common Text is sometimes unreliable or unavailable Users want “things,” not “stuff” –More than overall image properties –Traditional object recognition won’t work –Two choices: Rely on text to identify objects Look at regions (objects or parts of objects)

5. Large-scale systems: simple features Mainly overall color and texture, plus text Some segmentation QBIC [Flickner et al. ’95] WebSeek [Smith and Chang ’96] Virage [Gupta and Jain ’97] Photobook [Pentland et al. ’96] ImageRover [Sclaroff et al.’97] WebSeer [Frankel et al. ’96]

6. Other approaches Iconic matching [Jacobs et al. ’95] –Match user sketch to coarse representation –Look at overall image layout Spatial relationships [Lipson et al. ’97] –Relationships between superpixels for querying and classification Region-based approach [Ma & Manjunath ’98] –Semi-automatic segmentation

7. Classical object recognition won’t work Match geometric object model to image points Doesn’t apply to our domain –Designed for fixed, geometric objects –Relies on clean segmentation Survey: Binford ’82

8. Sample image retrieval systems Piction –News photos of people –Relies heavily on text –Captions guide image understanding WebSeek –Index images and videos on the Web –Relies heavily on text –Some content-based searching

9. Sample image retrieval systems (cont’d.) QBIC –General image/video collections –Use both content and textual information –User labeling is important Blobworld –Relies exclusively on image content –Find images regions corresponding to objects –Querying based on regions’ color, texture, simple shape

10. Piction: retrieval based on captions Collection: several hundred news photographs, mostly of people Use captions to identify who’s in the picture and where, then find matching faces Relies heavily on text

11. NLP derives constraints from caption Determine four types of information: –Object classes: Which proper-noun complexes (PNCs) are people? –Who/what is in the picture: Which people are in the picture? Which aren’t? –Spatial constraints: Where are these people in the picture? –Significant visual characteristics: What do they look like (gender, hair color, glasses, etc.)?

12. Use constraints in image interpretation Example: “Lillian Halasinski of Dunkirk stands with a portrait of Mother Mary Angela Truszkowka at a news conference in the Villa Maria Convent on Doat Street.”  First find frame, then look for faces inside and outside

13. Image understanding: locate and describe faces Locate human faces by finding three contours (hairline, left contour of face, right contour of face) Look at multiple scales Confirm by looking for face-specific features Neural net discriminates between male and female

14. Discussion How well does the NLP module work? –“Tom Smith and his wife Mary Jane prepare for the visit of President Clinton on Tuesday” How well does the face finder work? Does this system really need to use image content? –How much would we get from captions alone?

15. WebSeek: search for images on the Web Collection: 500,000 Web images and videos Filename and surrounding text determine subject Simple content-based techniques –Global color histograms –Color in localized regions

16. Collect and process images and videos Retrieve image or video Store visual features and textual information Generate icon (motion icon) to represent image (video) compactly

17. Classify images based on associated text Extract terms –URL: http://www.host.com/animals/dogs/poodle.gif –Alt text: –Hyperlink text: Sally the poodle Map terms to subjects using a semi-automatically constructed key-term dictionary Store terms, location in subject taxonomy

18. Content-based information Extract information from image –Global color histogram –Size, location, relationship of color regions Automatically determine image type: –Color photo, color graphic, gray image, B&W image Use content-based information when browsing within category hierarchy

19. Discussion How good is the text-based classification? Can we always count on having text? How much does the content-based information add in this case?

20. QBIC: image content plus annotation Collections: –General collections –Video clips –Fabric samples –etc. Retrieval based on color, texture, shape, sketch Relies (sometimes) on human annotation of image or objects

21. Populating the database Use any available text (title, subject, caption, etc.) User selects and labels important regions Store information about image content Extract key frames and describe video content

22. Video processing Detect shots –Pan over skyline  pan over Bay  zoom to bridge Generate representative frames –Capture all the background from the shot in one image Extract motion layers to facilitate segmentation

23. Query based on text and content Textual information: title, subject, object labels Global image properties: color and texture Object properties: color and shape Sketch: use the sketch as a template to find matches

24. Discussion QBIC’s shape description doesn’t capture the essence of objects, just the appearance of one view How much does QBIC rely on textual information?

25. Blobworld: represent image regions We want to find objects  look for coherent regions

26. Expectation-Maximization (EM) –Assign each pixel to a Gaussian in color/texture space  segmentation Model selection: Minimum Description Length –Prefer fewer Gaussians if performance is comparable Group pixels into regions

27. Describe regions’ color, texture, shape Color –Color histogram within region Texture –Contrast and directionality  stripes vs. spots vs. smooth Shape –Fourier descriptors of contour

28. Querying: let user see the representation Current systems act like black boxes –User can’t see what the computer sees –It’s not clear how parameters relate to the image User should interact with the representation –Helps in query formulation –Makes results understandable –Minimizes disappointment

29. Sample queries Query in collection of 10,000 imagesQuery Shape gives lower precision, closer matches

30. Experimental results Blobworld (two blobs) vs. global histograms (top 40 images)

31. Blobworld vs. global histograms Distinctive objects (tigers, cheetahs, zebras): –Blobworld does better Distinctive scenes (airplanes): –Global histograms do better –Adding blob size would help (  “large blue blob”) Others: –Both do somewhat poorly, but Blobworld has room to grow (shape, etc.)

32. Discussion How much can we accomplish using image content alone? –Today –In the future

33. Overall Discussion What are the tasks? Who are the actual users? What can we expect of the users? How much can we (should we) rely on text? How useful is content-based information? How good will image understanding need to be before it’s generally useful?