Content Based Image Retrieval (CBIR)
1. Visual Signature (Sensory, Semantic)
2. Similarity Measurement
3. Classification & Clustering
4. Search Paradigms
1. User Intent(Browser, Surfer, Searcher)
2. Data Scope
3. Query Processing
4. Result Visualization
Visual Similarity vs. Semantic Similarity
→ Application dependent
Domains for Visual Search:
– Narrow: Narrow image domains usually have limited variability and better-defined visual characteristics.
– Broad : tend to have high variability and unpredictability for the same underlying semantic concepts.
3 Types of Broad Image Search:
– search by association, where there is not clear intent at a picture, but instead the serch proceeds by iteratively refined browsing
– aimed search, where a specific picture is sought
– category search, where a single picture representative of a semantic class is sought
⇒ The overall goal is to bridge the semantic and sensorial gaps using the available visual features of images and relevant domain knowledge
In Sensorty property:
feature extraction geared at reducing the sensory gap due to the accidental distortions, cluster, occlusion, etc
– LUV color space – seem to coincide better with human vision than the basic RGB
– Texture: capture the granularity and repetitive patterns of surface
Every color has two perceptual attributes relate to its chromaticness or colorfulness:
1. Saturation, defined as the colorfulness of an area judged in proportion to its brightness
2. Chroma, the colorfulness of an area judged as a proportion of the brightness of a similarity illuminated white or highly transmitting area.
Saturation * Illuminant
Chromaticness(colorfulness) is the attribute of a visual sensation according to which the perceived color of an area appears to be more or less chromatic.