Developing semantic indices into large image databases is a challenging and important problem in content-based image retrieval. We address the problem of detecting objects in an image based on color and texture features. Specifically, we consider the following two problems of detecting sky and vegetation in outdoor images. An image is divided into 16 X 16 sub-blocks and color, texture, and position features are extracted form every sub-block. We demonstrate how a small set of codebook vectors, extracted from every sub- block. We demonstrate how a small set of codebook vectors, extracted from a learning vector quantizer, can be used to estimate the class-conditional densities of the low-level observed feature needed for the Bayesian methodology. The sky and vegetation detectors have been trained on over 400 color images from the Corel database. We achieve classification accuracies of over 94 percent for both the classifiers on the training data. We are currently extending our evaluation to a larger database of 1,700 images.