Image Mining In Image Retrieval

Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images.Image mining is rapidly gaining attention among researchers in the field of data mining, information retrieval, and multimedia databases because of its potential in dis-covering useful image patterns that may push the various research fields to new fron-tiers. The main purpose of this paper is to explore the usage of image mining technique in the field of image retrieval.Image retrieval in general can be divided into example-based image retrieval and text-based image retrieval.Among example-based image retrieval, image near-duplicate(IND) retrieval has a vast scene of application. We first analysis the major problem in IND retrieval based on Bag-of-Words model as "visual polysemy and synonymy phenomenon". To eliminate this phenomenon, we propose using associate rule mining to find "visual pattern". We propose and compare different usages of visual pattern. Experiments on benchmark dataset prove our proposed method is superior to classic Bag-of-Words model.Among the text-based image retrieval, using surrounding text as image's keywords and building index is the most commonly used method in commercial image search engine. However, because surrounding text is often associated with a lot of noise, image retrieval results can not meet the needs of users very well. To solve a location image retrieval task, we first define a measurement for image, namely, geographical relevance and then use it to rank the returning images. To obtain images'geographical relevance, we designed a online game to gather user's knowledge about image and location. We then use co-location mining algorithm to find the similar location, image geographical relevance and image region's geographical relevance. The comparison with a commercial search engine (Live search) confirmed our proposed algorithm is useful in improving location image retrieval's performance.Another direction in text-based image retrieval is automatic image annotation. Au-tomatic image annotation (also known as automatic image tagging) is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. The key problem in the field is the semantic gap be-tween feature and semantic concept. Modeling user's attention is one feasible solution to eliminate the semantic gap. We use concept mining technical in a image annota-tion task. We assume the images in one group share one "style". Mining and using this "style" could improve the annotation precision and consequently improve image retrieval based on auto-annotation. Experiments on the real word datasets prove our assumption.