Grouping WWW Image Search Results by Novel Inhomogeneous Clustering Method

In this paper, a novel inhomogeneous clustering method is proposed for grouping web images. It is used to re-organize the search result of web image search engines into a hierarchical structure so that the users can conveniently browse the search result. This method takes into account various features associated with web images, and treats them in different ways. For the surrounding text extracted from the containing web pages, co-clustering approach is adopted; for low-level features of the image content and other features, one-way clustering approach is adopted. The clustering results of different approaches are combined together to produce the final image groups. Experimental results demonstrate the effectiveness of the proposed method

TICAL - a web-tool for multivariate image clustering and data topology preserving visualization

In life science research bioimaging is often used to study two kinds of features in a sample simultaneously: morphology and co-location of molecular components. While bioimaging technology is rapidly proposing and improving new multidimensional imaging platforms, bioimage informatics has to keep pace in order to develop algorithmic approaches to support biology experts in the complex task of data analysis. One particular problem is the availability and applicability of sophisticated image analysis algorithms via the web so different users can apply the same algorithms to their data (sometimes even to the same data to get the same results) and independently from her/his whereabouts and from the technical features of her/his computer. In this paper we describe TICAL, a visual data mining approach to multivariate microscopy analysis which can be applied fully through the web.We describe the algorithmic approach, the software concept and present results obtained for different example images

Towards Semantic Clustering–A Brief Overview

Image clustering is an important technology which helps users to get hold of the large amount of online visual information, especially after the rapid growth of the Web. This paper focuses on image clustering methods and their application in image collection or online image repository. Current progress of image clustering related to image retrieval and image annotation are summarized and some open problems are discussed. Related works are summarized based on the problems addressed, which are image segmentation, compact representation of image set, search space reduction, and semantic gap. Issues are also identified in current progress and semantic clustering is conjectured to be the potential trend. Our framework of semantic clustering as well as the main abstraction levels involved is briefly discussed

Structure Preserving Large Imagery Reconstruction

With the explosive growth of web-based cameras and mobile devices, billions of photographs are uploaded to the internet. We can trivially collect a huge number of photo streams for various goals, such as image clustering, 3D scene reconstruction, and other big data applications. However, such tasks are not easy due to the fact the retrieved photos can have large variations in their view perspectives, resolutions, lighting, noises, and distortions. Fur-thermore, with the occlusion of unexpected objects like people, vehicles, it is even more challenging to find feature correspondences and reconstruct re-alistic scenes. In this paper, we propose a structure-based image completion algorithm for object removal that produces visually plausible content with consistent structure and scene texture. We use an edge matching technique to infer the potential structure of the unknown region. Driven by the estimated structure, texture synthesis is performed automatically along the estimated curves. We evaluate the proposed method on different types of images: from highly structured indoor environment to natural scenes. Our experimental results demonstrate satisfactory performance that can be potentially used for subsequent big data processing, such as image localization, object retrieval, and scene reconstruction. Our experiments show that this approach achieves favorable results that outperform existing state-of-the-art techniques

Hierarchical clustering-based navigation of image search results

Usually, the image search results contain multiple topics on semantic level and even semantically consistent images have diverse appearances on visual level. How to organize the results into semantically and visually consistent clusters becomes a necessary task to facilitate users ’ navigation. To attack this, HiCluster, an effective method to organize image search results is designed in this paper, which employs both textual and visual analysis. First, we extract some query-related key phrases to enumerate specific semantics of the given query and cluster them into some semantic clusters using K-lines-based clustering algorithm. Second, the resulting images corresponding to each key phrase are clustered with Bregman Bubble Clustering (BBC) algorithm, which partially groups images in the whole set while discarding some scattered noisy ones. At last, a novel user interface (UI) is designed to provide users with the diverse and helpful information based on the hierarchical clustering structure. Experiments on web images demonstrate the effectiveness and potential of the system