計算機專業(yè)英語

1、Chapter 11 Content-based Image Retrieval（CBIR)Think AboutWhat is content-based image retrieval? What does the term “content” mean?What are low level image retrieval, region based image retrieval, and semantic image retrieval respectively?From the historic overview, how has CBIR evolved?What does it

2、mean by multimedia information retrieval ? What Research Areas Are Involved In? Computer vision, pattern recognition, image processing, data mining, machine learning, human-computer interaction, artificial intelligence Application: digital museum/libraries, safety of society, image/video copy detect

3、ion, GIS, medicine, education, entertainment, WWW, to name just a fewDigital Image Retrieval Search for digital images in large databases First generation: laborious, subjectiveMetadata (captions or keywords) Image Second generation (content-based): objectiveImage contents Image Current way (semanti

4、c): subjective + objectiveImage contents + semantic feature Image Our way: Image contents + semantic feature + keywords/captions Image Semantic gapWhat Is Content-based Image Retrieval “Content-based” means that the search will analyze the actual contents of the image. The term “content” in this con

5、text might refer colors, shapes, textures, or any other information that can be derived from the image itselfLow Level Image Retrieval Color Examining images based on the colors they contain is one of the most widely used techniques because it does not depend on image size or orientation. Color sear

6、ches will usually involve comparing color histograms, though this is not the only technique in practiceColor SpaceRGBLightness (亮度，即明暗）Hue（色調(diào)，即光的顏色）Saturation（飽和度，即顏色的深淺）Chrominance (色度）65.738129.05725.06416137.94574.494112.439128256112.43994.15418.285128YRCbGCrBLow Level Image Retrieval Shape Shape

7、 does not refer to the shape of an image but to the shape of a particular region that is being sought out. Shapes will often be determined first applying segmentation or edge detection to an image. In some cases accurate shape detection will require human intervention because methods like segmentati

8、on are very difficult to completely automate.Edge Detection Low Level Image Retrieval Texture Texture measures look for visual patterns in images and how they are spatially defined. Textures are represented by texels which are then placed into a number of sets, depending on how many textures are det

9、ected in the image. These sets not only define the texture, but also where in the image the texture is locatedTexture Texture Coarseness (粗糙度） Contrast (對比度） Directionality(方向度） Linearity(線性度） Regularity(規(guī)整度） Roughness(粗略度）Region Based Image Retrieval Semantic Image Retrieval Human judgement of imag

10、e similarity is subjective. Therefore latest research focus on deriving semantic features using machine learning techniques to narrow down the semantic gapSemantic ModelLow level features (mixture of color, shape, texture, e.g. red circle)Objects, e.g. a manSpatial relationship between the objects,

11、e.g. a man in front of the houseEnvironment, e.g. sandAction, e.g. runFeeling, e.g. happySemantic ModelLow level features: color, shapeObjects: person, ball Spatial relationship: persons positionsEnvironment: sand, blue skyAction: play volleyballFeeling: relax, happySemantic Feature Retrieval Automa

12、tic image annotation Build a semantic spaceContent-based Image Retrieval From the above historic overview, it can be seen how CBIR has evolved from low level image retrieval to region based image retrieval and to semantic image retrievalDatabasesStructure of CBIRDigital imagesFeature extractionUserQ

13、uery interfaceSearch engineImage databaseFeature databaseKnowledge databaseImage Retrieval Methods Query by external pictorial example Brand search, finger mark search Query by internal pictorial example Query by sketch Keywords/captions Combination of the above methods EvaluationbaaPcaaR d bc adbbFPrecisionRecallPVREffectivenessEfficiencyFlexibilityA User Interface of Image Retrieval Future Dir