Searching Query by Color Content of an Image Using Independent Component Analysis

Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision to the image retrieval problem, that is, the problem of searching for digital images in large databases. In CBIR each image which is stored in the database has its features extracted and compared to the features of the query image. The features that are to be used by the computer should correspond directly to routine notions of vision like color, texture, pattern and shape. In Content-based the search will analyze the actual contents of the image based on various parameters like color, shape, texture, or any other information which can be derived from the image itself. A major problem of feature-based characterizations of visual data is the high dimensionality of the feature spaces. The feature space becomes increasingly difficult to index efficiently with increased dimensionality. If the features are properly chosen, they may lend well to a natural hierarchy in indexing, or be constructed from a more advantageous space, which can be efficiently indexed. Many indexing techniques are based on global features distribution such as Gabor Wavelets. [1]. In this paper we present an approach for global feature extraction using an technique known as Independent Component Analysis (ICA). A comparative study is done between ICA feature vectors and Gabor feature vectors for 180 different texture and natural images in a databank. Result analysis show that extracting color and texture information by ICA provides significantly improved results in terms of retrieval accuracy, computational complexity and storage space of feature vectors as compared to Gabor approaches.