Content-based image retrieval

About: Content-based image retrieval is a research topic. Over the lifetime, 6916 publications have been published within this topic receiving 150696 citations. The topic is also known as: CBIR.

Content based image retrieval and information theroy: a general approach

Abstract: A fundamental aspect of content-based image retrieval (CBIR) is the extraction and the representation of a visual feature that is an effective discriminant between pairs of images. Among the many visual features that have been studied, the distribution of color pixels in an image is the most common visual feature studied. The standard representation of color for content-based indexing in image databases is the color histogram. Vector-based distance functions are used to compute the similarity between two images as the distance between points in the color histogram space. This paper proposes an alternative real valued representation of color based on the information theoretic concept of entropy. A theoretical presentation of image entropy is accompanied by a practical description of the merits and limitations of image entropy compared to color histograms. Specifically, the L1 norm for color histograms is shown to provide an upper bound on the difference between image entropy values. Our initial results suggest that image entropy is a promising approach to image description and representation.

Using Long-Term Learning to Improve Efficiency of Content-Based Image Retrieval

Abstract: Content-based image retrieval (CBIR) is an emerging re- search field, studying retrieval of images from unannotated databases. In CBIR, images are indexed on the basis of low-level statistical fea- tures that can be automatically derived from the images. Due to the gap between high-level semantic concepts and low-level visual features, the performance of CBIR applications often remains quite modest. One method for improving CBIR results is to try to learn the user's pref- erences with intra-query learning methods such as relevance feedback. However, relevance feedback provides user interaction information which can automatically be used also in long-term or inter-query learning. In this paper, a method for using long-term learning in our PicSOM system is presented. The performed experiments show that the system readily supports the presented user interaction feature and that the eciency of

Content based image retrieval

Abstract: A content based image retrieval system that extracts images from a database of images by constructing a query set of features and displaying images that have a minimum dissimilarity metric from images in the database. The dissimilarity metric is a weighted summation of distances between features in the query set and features of the images in the database. The method is useful for image searching such as web-based image retrieval and facial recognition.

How to add content-based image retrieval capability in a PACS

Abstract: This paper presents a new picture archiving and communication system (PACS), called cbPACS (content-based PACS), which has content-based image retrieval resources. cbPACS answers similarity (range and nearest-neighbor) queries, taking advantage of a metric access method embedded into the image database manager. The images are compared via their features, which are extracted by an image processing system module. The system works on features based on the color distribution of the images through normalized histograms as well as metric histograms. Metric histograms are invariant with regard to scale, translation and rotation of images and also to brightness transformations. cbPACS is prepared to integrate new image features, based on the texture and shape of the main objects in the image.