Content-Based Image Retrieval (CBIR) is an approach of retrieving similar images from a large image database. Recently CBIR poses new challenges in semantic categorization the images. Different feature extraction technique have been proposed to overcome breach problems, however these methods suffer several shortcomings. This paper contributes retrieval system extract local features based on fus...

Nowadays the information retrieval (from the Internet and off-line sources) is one of the major research areas in Computer Science. The main criteria of a successful search are the high relevance of search query information and fast response time. Traditional search engines typically use an approach «Bag of words» based on statistical methods to search for information. This approach takes prece...

Unlike journal corpora, which are supposed to be carefully reviewed before being published, the quality of documents in a patient record are often corrupted by mispelled words and conventional graphies or abbreviations. After a survey of the domain, the paper focuses on evaluating the effect of such corruption on an information retrieval (IR) engine. The IR system uses a classical bag of words ...

In this paper we describe our participation in the 2010 CLEF-IP Prior Art Retrieval task where we examined the impact of information in different sections of patent documents, namely the title, abstract, claims, description and IPC-R sections, on the retrieval and re-ranking of patent documents. Using a standard bag-of-words approach in Lemur we found that the IPC-R sections are the most inform...

Although more and more language pairs are covered by machine translation services, there are still many pairs that lack translation resources. Cross-language information retrieval (CLIR) is an application which needs translation functionality of a relatively low level of sophistication since current models for information retrieval (IR) are still based on a bag-of-words. The Web provides a vast...

In this paper, a new method is proposed for object-based image retrieval. The user supplies a query object by selecting a region from a query image, and the system returns a ranked list of images that contain the same object, retrieved from a large image database. The main outcomes of this research are as follows: (1) An novel objectbased image retrieval framework that integrates effective pret...

In this paper we address the following problem in web document and information retrieval (IR): How can we use long-term context information to gain better IR performance? Unlike common IR methods that use bag of words representation for queries and documents, we treat them as a sequence of words and use long short term memory (LSTM) to capture contextual dependencies. To the best of our knowled...

We present a method for supervised learning of shape descriptors for shape retrieval applications. Many contentbased shape retrieval approaches follow the bag-of-features (BoF) paradigm commonly used in text and image retrieval by first computing local shape descriptors, and then representing them in a ‘geometric dictionary’ using vector quantization. A major drawback of such approaches is that...

Convolutional Neural Network (NN) has gained a lot of attention of the researchers due to its high accuracy in classification and feature learning. In this paper, we evaluated the performance of CNN used as feature for image retrieval with the gold standard feature, aka SIFT. Experiments are conducted on famous Oxford 5k data-set. The mAP of SIFT and CNN is 0.6279 and 0.5284, respectively. The ...

Two new methods for large scale image retrieval are proposed, showing that the classical ranking of images based on similarity addresses only one of possible user requirements. The novel retrieval methods add zoom-in and zoom-out capabilities and answer the “What is this?” and “Where is this?” questions. The functionality is obtained by modifying the scoring and ranking functions of a standard ...