Computational geometry.

Content-based image retrieval using color and texture fused features

Techniques are provided for searching on three dimensional (3D) objects across large, distributed repositories of 3D models. 3D shapes are created for input to a search system; optionally user-defined similarity criterion is used, and search results are interactively navigated and feedback received for modifying the accuracy of the search results. Search input can also be given by picking 3D models from a cluster map or by providing the orthographic views for the 3D model. Feedback can be given by a searcher as to which models are similar and which are not. Various techniques adjust the search results according to the feedback given by the searcher and present the new search results to the searcher.

Methods, systems, and data structures for performing searches on three dimensional objects

Image collections are getting larger and larger. To access those collections, systems for managing, searching, and browsing are necessary. Visualization plays an essential role in such systems. Existing visualization systems do not analyze all the problems occurring when dealing with large visual collections. In this paper, we make these problems explicit. From there, we establish three general requirements: overview, visibility, and structure preservation. Solutions for each requirement are proposed, as well as functions balancing the different requirements. We present an optimal visualization scheme, supporting users in interacting with large image collections. Experimental results with a collection of 10,000 Corel images, using simulated user actions, show that the proposed scheme significantly improves performance for a given task compared to the 2D grid-based visualizations commonly used in content-based image retrieval.

/pdf/interactive-access-to-large-image-collections-using-4668aqt3d9.pdf

Interactive access to large image collections using similarity-based visualization

The problem of content based image retrieval (CBIR) has traditionally been investigated within a framework that emphasises the explicit formulation of a query: users initiate an automated search for relevant images by submitting an image or draw a sketch that exemplifies their information need. Often, relevance feedback is incorporated as a post-retrieval step for optimising the way evidence from different visual features is combined. While this sustained methodological focus has helped CBIR to mature, it has also brought out its limitations more clearly: There is often little support for exploratory search and scaling to very large collections is problematic. Moreover, the assumption that users are always able to formulate an appropriate query is questionable. An effective, albeit much less studied, method of accessing image collections based on visual content is that of browsing. The aim of this survey paper is to provide a structured overview of the different models that have been explored over the last one to two decades, to highlight the particular challenges of the browsing approach and to focus attention on a few interesting issues that warrant more intense research.

http://www.daniel-heesch.com/papers/heesch-08a.pdf

A survey of browsing models for content based image retrieval

Digital images and videos have an increasingly important role in today’s telecommunication and our everyday life in modern information society. The past few years witnessed a proliferation of content-based image retrieval techniques. Images are typically characterized by intrinsic attributes of images such as color, texture, and shape. However, the potential of integrating these techniques with visualization and data-mining techniques has yet been fully explored. Users should be able to explore images in a database or video clips by visual similarities. In this article, we explore the synergy between Pathfinder networks and content-based information retrieval techniques. Salient structures of images are revealed through visualization models derived from features extracted from images. Visualizations are generated from three feature classes of the well-known QBIC system: color, layout, and texture.

http://www.pages.drexel.edu/~cc345/papers/wcc2000-iip-100.pdf

Similarity-Based Image Browsing

http://www.bmva.org/bmvc/1999/papers/37.pdf

Incorporating shape into histograms for CBIR

The proliferation of content based image retrieval techniques has highlighted the need to understand the relationship between image clustering based on low-level image features and image clustering made by human users. In conventional image retrieval systems, images are typically characterized by a range of features such as color, texture, and shape. However, little is known to what extent these low-level features can be effectively combined with information visualization techniques such that users may explore images in a digital library according to visual similarities. The authors compare and analyze a number of Pathfinder networks of images generated based on such features. Salient structures of images are visualized according to features extracted from color, texture, and shape orientation. Implications for visualizing and constructing hypermedia systems are discussed.

/pdf/content-based-image-visualization-2ansrel3by.pdf

Content-based image visualization

Shape measures for image retrieval

One of the main goals in content based image retrieval (CBIR) is to incorporate shape into the process in a reliable manner. In order to overcome the difficulties of directly obtaining shape information (in particular avoiding region segmentation) we develop several shape measures that tackle the problem in an indirect manner, requiring only a minimal amount of segmentation. A histogram-based scheme is then used, maintaining low complexity with high efficiency and robustness. The obtained results showed that the synergy of the shape measures worked out providing an improvement over the colour histogram.

/pdf/shape-measures-for-image-retrieval-1pt8a3nj8g.pdf

George Gagaudakis

Papers

Similarity-Based Image Browsing

Incorporating shape into histograms for CBIR

Content-based image visualization

Shape measures for image retrieval

Shape measures for image retrieval