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Spectral Covariance and Fuzzy Regions for Image Indexing

A. Dimai and M. Stricker
BIWI-TR-173, 1996
Image Science Lab, ETH Zurich


To improve the discrimination power of color indexing techniques we encode a minimal amount of spatial information in the index. We propose an approach that lies between uniformly tesselating the images with rectangular regions and relying on fully segmented images. For each image we define 5 partially overlapping, fuzzy regions. In the index we store for each region in an image its average color and the covariance matrix of the color distribution. These features are relatively insensitive to small translations and small rotations of an image because they are extracted from fuzzy regions. To retrieve images we define a function which measures the similarity of two color features. Invariance of retrieval results with respect to the typical image rotations of 90 degrees around the center of the image is guaranteed because our feature similarity function exploits the spatial arrangement of the 5 image regions. Finally, we propose two measures to evaluate the performance of image indexing techniques. We present experimental results using an image database which contains more than 11,000 color images. Our experiments demonstrate clearly that our weak encoding of spatial information significantly increases the discrimination power of the index compared to color indexing techniques that incorporate no spatial information or work with simpler color features.

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  author = {A. Dimai and M. Stricker},
  title = {Spectral Covariance and Fuzzy Regions for Image Indexing},
  year = {1996},
  number = {BIWI-TR-173},
  institution = {Image Science Lab, ETH Zurich},
  keywords = {image indexing, color indexing, color distribution features, fuzzy sets, performance evaluation}