Image Searching Tool Using Category-Based Indexing

Searching for an object in a general image collection using current image retrieval systems, is still a problem. The retrieval results contain many unrelated images. In providing an effective and robust image database, objects in an image need to be extracted. Since the number of stored images can be very large, automation is an important aspect. Image indexing is a technique that extracts objects in an image automatically. The aim of this research is to propose a new object based indexing system based on extracting salient region representative from the image and categorising an image into different types. Different image has different characteristics and often require different image processing techniques. Currently, most content based image retrieval (CBIR) systems operate on all images, without pre-sorting these images into different types. This resulted in limitations on retrieval performance and accuracy. Categories described here are of statistical and syntactical descriptions rather than semantical. By analysing which features are dominant in an image, two outcomes will be obtained: category for that image and salient object. Identifying salient object further reduce the retrieval results into relevant images.