In this paper, a novel low-level IMAGE feature extraction and indexing scheme based on structure-texture IMAGE decomposition is presented. The main idea of this work is to decompose database IMAGEs to structure and texture sub-IMAGEs to decrease the destructive effects of simultaneous existence of structure and texture information in the IMAGE in indexing phase. It is also shown that precision in a typical content-based IMAGE RETRIEVAL system can considerably increase by combining the feature vectors extracted from structure and texture sub-IMAGEs. An IMAGE database containing 10000 IMAGEs of 82 different semantic groups is used to evaluate the proposed method. The results confirm the effectiveness of this method.

Several semantic IMAGE search schemes have been recently proposed to retrieve IMAGEs from the web. However, the query context is regularly ignored in these techniques and hence, many of the returned IMAGEs are not adequately relevant. In this paper, we make use of context to further confine the outcome of the semantic search engines. For this purpose, we propose a hybrid search engine which utilizes concept and context for retrieving precise results. In the proposed model, an ontology is exploited for annotating IMAGEs and accomplishing search process in the semantic level. Furthermore, the query of the user is modified with the concepts available in the ontology. Next, we make use of search context of the user and augment the query with the information extracted from the user’s context to additionally eliminate irrelevant results. Experimental results show that the combination of concept and context is effective in retrieving and presenting the most relevant results to the user.

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.

In this study, an IMAGE RETRIEVAL system is proposed based on complex network model. Assuming a prior IMAGE categorization, firstly, a multilayered complex network is constructed between the IMAGEs of each category according to the color, texture, and shape features. Secondly, by defining a meta-path as the way of connecting two IMAGEs in the network, a set of informative meta-paths are composed to find the similar IMAGEs by exploring the network. The established complex network provides an efficient way to benefit from the IMAGE correlations to enhance the similarity search of the IMAGEs. On the other hand, employing diverse meta-paths with different semantics leads to measuring the IMAGE similarities based on effective IMAGE features for each category. The primary results indicate the efficiency and validity of the proposed approach.

E-commerce plays an important role in the world economy. A wide variety of websites have been designed to provide the ability of searching different types of products. Carpet is such a product which cannot be addressed easily with a special code in markets due to the huge variety in its specifications such as layout, color, and texture. This paper introduces a content-based IMAGE RETRIEVAL system for carpet e-commerce application. This system helps development of the carpet e-commerce where an IMAGE can be used instead of any tags including codes or models. An IMAGE database containing various Persian carpet IMAGEs is also made for this application. Furthermore, several content-based IMAGE RETRIEVAL methods are studied and applied on the carpet database and inspiring by the evaluation results, two methods, QCLD and DCDIP are proposed for carpet e-commerce application. Simulation results show 3. 1% and 2. 3% decrease on the ANMRR value for the proposed QCLD and DCDIP methods respectively. RETRIEVAL running times also are reported 2. 84 and 8. 15 seconds for the QCLD and DCDIP methods. In overall, these results reflect higher RETRIEVAL performance for the proposed methods.