Content-Based Image Retrieval using the EMD algorithm

Technion - Israel Institute of Technology Department of Electrical Engineering The Vision Research and Image Science Laboratory Content-Based Image Retrieval using the EMD algorithm Igal Ioffe George Leifman Supervisor: Doron Shaked Winter-Spring 2000

Project Goal Source Image Color Image DB Similar Images • Estimate similarity between pairs of images • Order the images accounting to similarity to the source image by query

System Overview DB Imagefeatures Query Queryprocess Distance Similarimages Image

Overview:Images & Histograms

Overview: Distance • Minkowski-form distance (L2) • EMD – Earth Movers Distance

Overview: quantization • Summarizing the image content • Reducing high computation complexity Original Image (20154 colors) Quantized Image (15 colors) Quantized Image (5 colors)

Research Issues • Color Quantization algorithms • Quad Tree clustering • Different color spaces • EMD - Earth Movers Distance algorithm

Median Cut vs. Maximum Diversity • Maximum Diversity better than Median Cut for small number of colors (<10) Median Cut Maximum Diversity 3 colors 2 colors

Problems with Histogram

Quad Tree Clustering • Recursive cluster definition • Dynamic stop constraints

Q.Tree Clustering Examples

RGB color space • linear combination of red, green, blue • used to represent image pixels • CIE LAB color space • closer to human vision system Color Spaces

EMD • Bipartite network flow problem • Can be formalized as a well known transportation problem from linear programming field • Minimize- cost Efficient and fast Simplex based solutions

Principal Block Scheme • (a) Color Image Database • (b) Preprocess each image • (c) Store properties of each image in file • (d) Start data base navigation

DB Creator demo

DB Navigator demo

Results

Why Visual C++ ? Graphic user-friendly interface Faster thanMatlab C++ Object Oriented Design Patterns Usage of MFC: effective and convenient way to manipulate large database structures, information reordering and querying (files, strings, array, etc)

Code Optimizations Effective Cache Usage Decreasing data dependencies in out-of-order execution Loop Unrolling Using Multi-Threading to achieve performance gain on Multi-Processor systems

Code Optimizations - Examples struct Rec{ Key key; Rec *next; Data data; }; struct Rec{ Key key; Data data; Rec *next; }; for (i=0; i<N/2; i+=2) { acc1+=a[i]; acc2+=a[i+1]; } acc = acc1 + acc2; for (i=0; i<N; i++) { acc+=a[i]; }

Conclusions EMD captures wellperceptual similarity or dissimilarity of images Using both color histogram and image cluster map improves the results versus histogram alone There is no preferable color space, but their combination leads to better results

Issues For Further Research Including texture properties in image description Testing the application on very large image data bases (> 10000 images) Handling various images transformations, e.g. partial image, scaling, rotation More advanced image feature combination,including color,texture and position

Content-Based Image Retrieval using the EMD algorithm

Télécharger la présentation

Plus de contenu associé