Characteristics of weighted feature vector in content-based image retrieval applications

Color and texture feature vectors of an image are always considered to be an important attribute in content-based image retrieval system. Both of these feature vectors of an image can be combined for the performance enhancement of the content-based image retrieval system. One of the standard ways of extracting color feature from an image is to generate a color histogram. Using Haar Wavelet or Daubechies' wavelet the texture feature of an image can be extracted. These two feature vectors and the feature vectors in the database are normalized so that the value of a bin is always between [0, 1]. During retrieval, both color and texture feature vectors of query image is combined, weighted and compared with the color and texture feature vectors of each of the database images using Manhattan distance metric. The retrieved result is dependent on the weight given to each of the feature vector. We have done a detailed study of the performance of different combination of weights to color (w(c)) and texture (w(1)) features on a large database of images. Different combination weights are used in for evaluation and the results shows that texture feature vector weight (w(1)) in the range of w(c) +/-0.1 to w(c) +/-0.2 perform better than the other combinations.