This paper describes our participation at the ImageCLEF photographic retrieval task. We used our Xtrieval framework for the preparation and execution of the experiments. This year, we submitted 4 experiments in total. The experiments showed that our thesaurus based query expansions works well in improving the geometric mean average precision (GMAP) and binary preference (BPREF), but deteriorate...

Zur Beantwortung der Frage, warum Bildersuchen auf Schlüsselwörtern basieren, statt auf den bildimmanenten Informationen, werden Repräsentationsmöglichkeiten für Bildinformationen vorgestellt, die Personen in die Lage versetzen, Anfragen in Form von Bildinformationen statt Schlüsselwörtern zu formulieren. Die Bildinformationen sollen in einer Weise interpretiert werden, die dem subjektiven Empf...

In this article, a fabulous method for database retrieval is proposed.  The multi-resolution modified wavelet transform for each of image is computed and the standard deviation and average are utilized as the textural features. Then, the proposed modified bit-based color histogram and edge detectors were utilized to define the high level features. A feedback-based dynamic weighting of shap...

Rapid growth of World Wide Web has increased the interest towards image retrieval. Different groups need to find a desired image from a collection. The users may require access to the images, based on primitive features, such as color, texture or shape, or associated text. The technology to access these images has also accelerated phenomenally. The current approaches are broad and inter-discipl...

Object discovery is one of the steps to effective content-based image retrieval. If a vision system has the ability to identify potential objects in an image, it enables the user to phrase queries in terms of objects, and it enables the computer to search the image library for similar objects. This paper describes a physics-based approach to object discovery. The approach grows seed regions to ...

Traditional computer-based image retrieval techniques provide several mechanisms to retrieve images. Generally, there are two major approaches being used in image retrieval systems. The first approach is content-based image retrieval which aims to retrieve images based on their visual criteria. This approach typically utilizes visual features, such as color, shape, texture, and layout, to deter...

Most of the currently available image database systems provide a text-based retrieval function called keyword retrieval, where users specify `keywords' such as titles, attributes, and categories of themes. But many times it is not easy for users to specify suitable keywords for a particular retrieval. Besides, building a large image database with complete description of contents is a very dicu...

Despite the continuous development of features and mid-level representations, effectively and reliably measuring the similarity among images remains a challenging problem in image retrieval tasks. Once traditional measures consider only pairwise analysis, context-sensitive measures capable of exploiting the intrinsic manifold structure became indispensable for improving the retrieval performanc...

Content-Based Image Retrieval (CBIR) locates, retrieves and displays images alike to one given as a query, using a set of features. It demands accessible data in medical archives and from medical equipment, to infer meaning after some processing. A problem similar in some sense to the target image can aid clinicians. CBIR complements text-based retrieval and improves evidence-based diagnosis, a...