Iconic pictorial retrieval using multiple attributes and spatial relationships

This work is on the use of multiple attributes or features and spatial relationships, with the help of a user interface based on an iconic paradigm, to retrieve images represented by iconic pictures. An icon has texture, color, and text attributes. Texture is represented by three statistical textural properties, namely, coarseness, contrast, and directionality. For text, the vector space model is used. For color, a representation based on a modified color histogram method which is less storage-intensive is proposed. The final icon similarity is the combination of the attribute similarity values using a proven adaptive algorithm. 2-D strings and its variants are commonly used to represent spatial relationships and perform spatial reasoning. We extended the method to include similarity ranking by using different similarity functions for different spatial relationships and an efficient embedding algorithm. Furthermore, our method solves the problem of query expressiveness which all methods based on 2-D string representations suffer from.