Object-based Image Retrieval System Research Articles

An object-based image retrieval system for digital libraries

A novel approach to clustering for image segmentation and a new object-based image retrieval method are proposed. The clustering is achieved using the Fisher discriminant as an objective function. The objective function is improved by adding a spatial constraint that encourages neighboring pixels to take on the same class label. A six-dimensional feature vector is used for clustering by way of the combination of color and busyness features for each pixel. After clustering, the dominant segments in each class are chosen based on area and used to extract features for image retrieval. The color content is represented using a histogram, and Haar wavelets are used to represent the texture feature of each segment. The image retrieval is segment-based; the user can select a query segment to perform the retrieval and assign weights to the image features. The distance between two images is calculated using the distance between features of the constituent segments. Each image is ranked based on this distance with respect to the query image segment. The algorithm is applied to a pilot database of natural images and is shown to improve upon the conventional classification and retrieval methods. The proposed segmentation leads to a higher number of relevant images retrieved, 83.5% on average compared to 72.8 and 68.7% for the k-means clustering and the global retrieval methods, respectively.

Image Indexing and Retrieval Using Object-based Point Feature Maps

Multimedia data such as audios, images, and videos are semantically richer than standard alphanumeric data. Because of the nature of images as combinations of objects, content-based image retrieval should allow users to query by image objects with finer granularity than a whole image. In this paper, we address a web-based object-based image retrieval (OBIR) system . Its prototype implementation particularly explores image indexing and retrieval using object-based point feature maps. An important contribution of this work is its ability to allow a user to easily incorporate both low- and high-level semantics into an image query. This is accomplished through the inclusion of the spatial distribution of point-based image object features, the spatial distribution of the image objects themselves, and image object class identifiers. We introduce a generic image model, give our ideas on how to represent the low- and high-level semantics of an image object, discuss our notion of image object similarity, and define four types of image queries supported by the OBIR system. We also propose an application of our approach to neurological surgery training.