Shuyu Yang and Sunanda Mitra Texas Tech University Department of Electrical & Computer Engineering

1. Content Based Vector Coder for Information Retrieval Shuyu Yang and Sunanda Mitra Texas Tech University Department of Electrical & Computer Engineering Computer Vision & Image Analysis Laboratory(CVIAL)

2. Objectives • Allows fast content-based information retrieval using multiscale wavelet based universal codebook for vector quantization (WMVQ). • Develop an image-size-independent feature extraction method using WMVQ. • For better feature preservation, WMVQ is combined with scalar quantization (SQ) of large magnitude wavelet coefficients. • Reduce the size of the universal codebook and VQ encoding/decoding time Codebook training algorithms: • Adaptive Fuzzy Leader Clustering (AFLC) • Deterministic Annealing (DA)

3. Neural network ART-1 structure for initial input classification 3. Fuzzy C-means centroid and membership update 2. Vigilance test, cluster growing and updating with user interface thresholding, The value of tao determines the number of clusters. Adaptive Fuzzy Leader Clustering

4. y1=m, p(y1|x)=1, K=1 Optimize F, the Cost function: For  i: yi_pre=yi Get Cx|yi and Tci Lower temperature T=T With constraints: yK=yi+ p(yk)=p(yi)/2 p(yi)=p(yi)/2 Y N Split yi: K=K+1 T < Tci? N ||yi-yi_pre|| <R? ||yi|| Y Y N Get Cx|yi and Tci Stop K=Kmax ? Deterministic Annealing

5. AFLC vs. DA Performance Analysis: Accuracy & Execution Time Clustering accuracy: PSNR comparison Execution time comparison

6. 1st level 2nd level Low resolution sub-image 2nd level Horizontal Sub-image 1st level Horizontal Sub-image 2nd level Vertical Sub-image 2nd level Diagonal Sub-image 1st level Vertical Sub-image 1st level Diagonal Sub-image 2-level Wavelet decomposition Original Image 2 level decomposition Wavelet Transform Daub. (9,7) wavelet

7. Feature Extraction for Universal Codebook Generation • Vector dimension depends on the level of decomposition • Scalability: • Sample vectors generated from WT coeffs. Are independent of the image size • Sample vector magnitudes can be scaled to be in the same range, facilitating universal codebook generation • Explores inter-scale and intra-scale dependency Vector amplitude Vectors formed from WT coeffs. Example of vector amplitude distribution Vector dimension

8. - 1 2 3 30 31 Coding Scheme Residual SPIHT Lossless coding Output 1 Encoder Wavelet Transform Feature Extraction Table lookup 6 Codeword indices Lossless coding Output 2 Codebook training Codebook AFLC/DA clustering Wavelet Transform Feature Extraction Codeword indices Lossless decoding Table lookup Output 2 decoder Reconstructed image Inverse Wavelet Transform Feature reconstruction Residual SPIHT decode Lossless decoding + Output 1

9. Universal Codebook Training Data: 3D MR Images Some slices from the training data set

10. (b) VQ+SQ coded 0.36bits/pixel PSNR:40.87dB (e) SPIHT coded 0.37 bits/pixel PSNR: 40.86 dB (c) VQ+SQ coded 0.095 bits/pixel PSNR:32.51 dB (f) SPIHT coded 0.1266 bits/pixel PSNR: 32.53 dB (d) VQ+SQ coded 0.048 bits/pixel PSNR: 29.81 dB (g) SPIHT coded 0.07 bits/pixel PSNR: 28.87 dB Result: VQ decoded images vs. SPIHT (a) Original test image (MRI slice 6)

11. Result: VQ decoded images vs. SPIHT

12. Conclusions & Future Research • Through efficient clustering, WMVQ enables fast content-based information retrieval with universal codebook generation. • WMVQ improves reconstructed image quality and reduces coding complexity by combining with scalar quantization for important feature preservation at very low bit rates. • Further reduction in bit rate is possible by using structured VQ codebooks • Codebook can be refined by improving the sample-order dependent problem with AFLC