This paper summarizes our empirical study of cross-language and cross-media image retrieval at the CLEF image retrieval track (ImageCLEF2007). In this year, we participated in the ImageCLEF photo retrieval task, in which the goal of the retrieval task is to search natural photos by some query with both textual and visual information. In this paper, we study the empirical evaluations of our solu...

The retrieval using single feature has a certain limitation, which fails to comprehensively describe an image. Aiming at such retrieval defect, this paper proposes an image retrieval method integrating color and texture. Firstly, carry out image segmentation with uniformly-spaced method, and then extract color feature of each segmentation with weighting processing done; and then, extract textur...

Information retrieval is one of the most fundamental functions in this era information. There is ambiguity in the scope of interest of users, regarding image/video retrieval, since an image usually contains one or more main objects in focus, as well as other objects which are considered as ̳background‘. This ambiguity often reduces the accuracy of image-based retrieval such as query by image ex...

Traditional content-based image retrieval technology expresses the content of each image by feature vectors. Then image retrieval process begins by calculating the similarity between the image to search and the images in the database in terms of the corresponding feature vectors, next ranks the images in the database by a descending order of similarity, and finally outputs the desired top ones....

In this paper we introduce imageFARMER, a framework that allows information retrieval researchers and educators to develop and customize domain-specific content-based image retrieval systems with ease while developing a deeper understanding of the underlying representation of domainspecific image data. imageFARMER incorporates different aspects of image processing and content-based information ...

3) Learning Objectives At the end of the module, student will be able to 1) Explain the basics of Image Retrieval and its needs in digital libraries. 2) Explain the different Image Retrieval types. 3) Classify Content Based Image Retrieval based on various features and differentiate between the techniques being used. 4) Explain the various Image Retrieval systems in existence and evaluation str...

-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...

Most of the image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity. Image Retrieval technique to retrieve similar and relevant Computed Tomography (CT) images of lung from a large database of images. During the process of retrieval, a query image which contains the affected area / abnormal region is given as an in...

In order to improve the retrieval efficiency and accuracy of remote sensing image, and this paper proposed a remote sensing image retrieval algorithm based on MapReduce. Firstly, the image color and texture features of emote sensing are extracted, and then the Map function is used to compute similarity among the retrieval remote sensing images and the feature library he according to color, colo...

Feature coding and pooling as a key component of image retrieval have been widely studied over the past several years. Recently sparse coding with max-pooling is regarded as the state-of-the-art for image classification. However there is no comprehensive study concerning the application of sparse coding for image retrieval. In this paper, we first analyze the effects of different sampling strat...