Content-Based Image Categorization and Retrieval using Neural Networks

We propose a neural network based method for organizing images for content-based image retrieval. We use spectral histogram features, the histograms of filtered images to capture the spatial relationship among pixels as well as global appearance of images. We then find the optimal combination of spectral histogram features using optimal factor analysis to reduce the dimension of features and maximize the discrimination. The reduced features are then used as input to a multiple layer perceptron, which is trained to categorize images based on content using back propagation. For a query image, images are retrieved from different classes based on the categorization probability for the query image. Experimental results on a subset of Corel dataset demonstrate the effectiveness of the proposed method and comparisons show that the proposed method gives significant improvement over other methods.