6 research outputs found
IRFuM: Image Retrieval Via Fuzzy Modeling
To reduce the semantic gap in the content based image retrieval (CBIR) systems we propose a fuzzy rule base approach. By submitting a query to the proposed system, it first extracts its low-level features and then checks its rule base for determining the proper weight vector for its distance measure. It then uses this weight vector to determine what images are more similar to the query image. For the training purpose, an algorithm is provided by which the system adjusts its fuzzy rules' parameters by gathering the trainers' opinions on which and how much the image pairs are relevant. For further improving the performance of the system, a feature space dimensionality reduction method is also proposed. We compared the proposed method with some other common ones. Our experiments on a subset of the Corel database containing 59 600 images show that the proposed method is more precise than these compared methods based on the precision and recall criterions
A FEATURE RELEVANCE ESTIMATION METHOD FOR CONTENT-BASED IMAGE RETRIEVAL
Feature relevance estimation is one of the most successful techniques used for improving the retrieval results of a content-based image retrieval (CBIR) system based on users' feedbacks. In this class of approaches, the weights of the feature elements (FEs) are adjusted based on the relevance feedbacks (RFs) given by the users to reduce the so-called semantic gap in the underlying CBIR system. An analytical approach is proposed in this paper to convert the users' feedbacks to the appropriate FE weights by solving a constrained optimization problem. Experiments on a set of 11,000 images from the Corel database show that the proposed approach outperforms other existing short-term RF approaches reported in the literature. The proposed approach is also incorporated in two long-term RF methods and enhanced their performance.Relevance feedback, feature relevance estimation, constrained optimization
Content Based Radiographic Images Indexing and Retrieval Using Pattern Orientation Histogram
Introduction: Content Based Image Retrieval (CBIR) is a method of image searching and retrieval in a database. In medical applications, CBIR is a tool used by physicians to compare the previous and current medical images associated with patients pathological conditions. As the volume of pictorial information stored in medical image databases is in progress, efficient image indexing and retrieval is increasingly becoming a necessity. Materials and Methods: This paper presents a new content based radiographic image retrieval approach based on histogram of pattern orientations, namely pattern orientation histogram (POH). POH represents the spatial distribution of five different pattern orientations: vertical, horizontal, diagonal down/left, diagonal down/right and non-orientation. In this method, a given image is first divided into image-blocks and the frequency of each type of pattern is determined in each image-block. Then, local pattern histograms for each of these image-blocks are computed. Results: The method was compared to two well known texture-based image retrieval methods: Tamura and Edge Histogram Descriptors (EHD) in MPEG-7 standard. Experimental results based on 10000 IRMA radiography image dataset, demonstrate that POH provides better precision and recall rates compared to Tamura and EHD. For some images, the recall and precision rates obtained by POH are, respectively, 48% and 18% better than the best of the two above mentioned methods. Discussion and Conclusion: Since we exploit the absolute location of the pattern in the image as well as its global composition, the proposed matching method can retrieve semantically similar medical images