This paper proposes a classification network to image semantic retrieval (NIST) framework to counter the image retrieval challenge. Our approach leverages the successful classification network GoogleNet based on Convolutional Neural Networks to obtain the semantic feature matrix which contains the serial number of classes and corresponding probabilities. Compared with traditional image retrieva...

The conventional image retrieval methods like Google, Bingo, Yahoo are based on the on textual annotation of images to access the large collection of relevant database images. Then Content Based Image Retrieval (CBIR) is a technique, which takes visual contents of image to retrieve relevant images from large databases. In Content Based Image Retrieval, there is a semantic gap between the low le...

With the advance of multimedia and diagnostic images technologies, the number of radiographic images is increasing constantly. The medical field demands sophisticated systems for search and retrieval of the produced multimedia document. This paper presents an ongoing research that focuses on the semantic content of radiographic image documents to facilitate semantic-based radiographic image ind...

We present an information fusion approach to designing a semantics-sensitive image retrieval system. Our approach is based on the three different types of classifiers, which extract and provide semantic cues of image regions. This local information from the proposed classifiers is fused together to generate the semantic labels of images. The experimental results show that our information fusion...

The biggest problem in the research of Content Based Image Retrieval (CBIR) is bridge the gap between low-level features and high-level semantics. , Still many shortcomings for image retrieval system only with the low level visual features due to the semantic space. It is better for the relevance feedback based on the user involvement in image retrieval system. By using the help of user's ...

With the advance of multimedia technologies in general and diagnostic images technologies specifically, the number of radiographic images is constantly escalating in the biomedical field. This field demands sophisticated systems for management and effective search and retrieval of the radiographic images produced. This paper presents a semantic content-based radiographic image retrieval system ...

-In this paper, a new method for intermediate features based image retrieval is proposed. Image database is constructed with low level texture features obtained from Gray Level Co-Occurrence Matrix (GLCM). Semantic level queries from the user mapped to the low level features at retrieval time to retrieve the required images. Artificial Neural Network (ANN) is used in the next steps after receiv...

A solution to the problem of image retrieval based on queryby-semantic-example (QBSE) is presented. QBSE extends the idea of query-by-example to the domain of semantic image representations. A semantic vocabulary is first defined, and a semantic retrieval system is trained to label each image with the posterior probability of appearance of each concept in the vocabulary. The resulting vector is...

-Markovsemantic indexing algorithm based on the controlled Markov chain modeling framework. Controlled Markov chain models are used to describe the temporal evolution of low-level visual descriptors extracted from the semantic indexing model. Propose a semantic indexing algorithm which uses both text and image retrieval system.The entire user Queries selected by randomly. An image retrieval sys...

In order to improve the retrieval accuracy of content-based image retrieval systems, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the ‘semantic gap’ between the visual features and the richness of human semantics. This paper attempts to provide a comprehensive survey of the recent technical achievements in high-level semantic-b...