Content-based Image Retrieval (CBIR)

regions 25 Object Detection: Rowley’s Face Finder 1. convert to gray scale 2. normalize for lighting* 3. histogram equalization 4. apply neural net(s) trained on 16K images What data is fed to the classifier? 32 x 32 windows in a pyramid structure * Like first step in Laws algorithm, p. 220 26 Fleck and Forsyth’s Flesh Detector The “Finding Naked People” Paper • Convert RGB to HSI • Use the intensity component to compute a texture map texture = med2 ( | I med1(I) | ) • If a pixel falls into either of the following ranges, it’s a potential skin pixel texture < 5, 110 < hue < 150, 20 < saturation < 60 texture < 5, 130 < hue < 170, 30 < saturation < 130 median filters of radii 4 and 6 Look for LARGE areas that satisfy this to identify pornography. See Transparencies 27 Wavelet Approach Idea: use a wavelet decomposition to represent images What are wavelets? • compression scheme • uses a set of 2D basis functions • representation is a set of coefficients, one for each basis function 28 Jacobs, Finkelstein, Salesin Method for Image Retrieval (1995) 1. Use YIQ color space 2. Use Haar wavelets 3. 128 x 128 images yield 16,384 coefficients x 3 color channels 4. Truncate by keeping the 40-60 largest coefficients (make the rest 0) 5. Quantize to 2 values (+1 for positive, -1 for negative) 29 JFS Distance Metric d(I,Q) = w00 | Q[0,0] I[0,0] | + ∑ wij | Q’[i,j] I’[i,j] | ij where the w’s are weights, Q[0,0] and I[0,0] are scaling coefficients related to average image intensity, Q’[i,j] and I’[i,j] are the truncated, quantized coefficients.