Similarity-based image browsing (original) (raw)

Content-based image visualisation

The proliferation of content-based image retrieval techniques has highlighted the need to understand the relationship between image clustering based on low-Ievel image features and image clustering made by human users. In conventional image retrieval systems, images are typically characterized by a range offeatures such as color, texture, and shape. However, little is known to what extent these low-Ievel features can be effectively combined with information visualization techniques such that users may explore images in a digital library according to visual similarities. In this article, we compared and analyzed 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.

Content-based image visualization

… , 2000. Proceedings. IEEE …, 2000

Pathfinder Networks for Content Based Image Retrieval Based on Automated Shape Feature Discovery

IEEE Sixth International Symposium on Multimedia Software Engineering, 2004

In this paper, we present a computer-assisted image browsing system based on Pathfinder Networks. Similarity of images to one another is determined through a proposed method of automatic shape feature discovery. Local features are generated by clustering small (on the order of 10 by 10 pixels) binary image blocks culled from the edge analysis of images in the database and using the cluster means as the local feature detectors. The clustering method for the binary image blocks is based on the Hausdorff metric of distance between sets of points. Relationships between local features then determine the similarity between images. Pathfinder Networks are then used to visually represent similarity between images. The results are presented on a database containing three categories of images.

Visual structures for image browsing

2003

Content-Based Image Retrieval (CBIR) presents several challenges and has been subject to extensive research from many domains, such as image processing or database systems. Database researchers are concerned with indexing and querying, whereas image processing experts worry about extracting appropriate image descriptors. Comparatively little work has been done on designing user interfaces for CBIR systems. This, in turn, has a profound effect on these systems since the concept of image similarity is strongly influenced by user perception. This paper describes an initial effort to fill this gap, combining recent research in CBIR and Information Visualization, studied from a Human-Computer Interface perspective. It presents two visualization techniques based on Spiral and Concentric Rings implemented in a CBIR system to explore query results. The approach is centered on keeping user focus on both the query image, and the most similar retrieved images. Experiments conducted so far suggest that the proposed visualization strategies improves system usability.

Content-based multimedia information retrieval: State of the art and challenges

ACM Transactions on Multimedia Computing, Communications, and Applications, 2006

Extending beyond the boundaries of science, art, and culture, content-based multimedia information retrieval provides new paradigms and methods for searching through the myriad variety of media all over the world. This survey reviews 100+ recent articles on content-based multimedia information retrieval and discusses their role in current research directions which include browsing and search paradigms, user studies, affective computing, learning, semantic queries, new features and media types, high performance indexing, and evaluation techniques. Based on the current state of the art, we discuss the major challenges for the future.

A Survey on Content-based Visual Information Retrieval

2020

Images have always been seen as an effective medium for visual data presentation. In recent years, a tremendous combination of images and videos have been grown up rapidly due to technology evolution. Content-Based Visual Information Retrieval (CBVIR), which is the process of searching for images via the end user's predefined specific pattern (hand sketch, camera capture, or web scrawled). CBVIR is still far away from achieving objective satisfaction due to image content-based search engines (for ex. Google image-based search) still not completely satisfying. This problem occurs because of the semantic gap between low and high visual level features representation of the image. In this paper, The state-ofart CBVIR techniques for multi-purpose applications are survived. The architecture of the promising CBVIR pipelines in recent decades, which witness the arising of computer vision is highlighted. Mathematical, machine, and deep learning-based CBVIR systems are introduced. Althoug...

Bridging the Semantic Gap in Content Based Image Retrieval

IJCSMC, 2018

Image content on the Web is increasing exponentially. As a result, there is a need for image retrieval systems. Historically, there have been two methodologies, text-based and content-based. In the text-based approach, query systems retrieve images that have been manually annotated using key words. This approach can be problematic: it is labor-intensive and maybe biased according to the subjectivity of the observer. Content based image retrieval (CBIR) searches and retrieves digital images in large databases by analysis of derived-image features. CBIR systems typically use the characteristics of color, texture, shape and their combination for definition of features. Similarity measures that originated in the preceding text-based era are commonly used. However, CBIR struggles with bridging the semantic gap, defined as the division between high-level complexity of CBIR and human perception and the low-level implementation features and techniques. In this paper, CBIR is reviewed in a broad context. Newer approaches is feature generation and similarity measures are detailed with representative studies addressing their efficacy. Color-texture moments, columns-of-interest, harmonysymmetry-geometry, SIFT (Scale Invariant Feature Transform), and SURF (Speeded Up Robust Features) are presented as alternative feature generation modalities. Graph matching, Earth Mover’s Distance, and relevance feedback are discussed with the realm of similarity. We conclude that while CBIR is evolving and continues to slowly close the semantic gap, addressing the complexity of human perception remains a challenge.

2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763), 2004

In any CBIR system, visualization is important, either to show the final result to the user or to form the basis for interaction. Advanced systems use 2-dimensional similarity based visualization which show not only the information of one image itself but also the relations between images. A problem in interactive 2D visualization is the overlap between the images displayed. This obviously reduces the search capability. Simply spreading the images on the screen space will not preserve the relations between them. In this paper, we propose a visualization scheme which reduces the overlap as well as preserves the general distribution of the images displayed. Results show that an effective balance between display of structures and limited overlap can be achieved.

IJERT-A Survey on Content-based Visual Information Retrieval

International Journal of Engineering Research and Technology (IJERT), 2020

https://www.ijert.org/a-survey-on-content-based-visual-information-retrieval https://www.ijert.org/research/a-survey-on-content-based-visual-information-retrieval-IJERTV9IS100224.pdf Images have always been seen as an effective medium for visual data presentation. In recent years, a tremendous combination of images and videos have been grown up rapidly due to technology evolution. Content-Based Visual Information Retrieval (CBVIR), which is the process of searching for images via the end user's predefined specific pattern (hand sketch, camera capture, or web scrawled). CBVIR is still far away from achieving objective satisfaction due to image content-based search engines (for ex. Google image-based search) still not completely satisfying. This problem occurs because of the semantic gap between low and high visual level features representation of the image. In this paper, The state-of-art CBVIR techniques for multipurpose applications are survived. The architecture of the promising CBVIR pipelines in recent decades, which witness the arising of computer vision is highlighted. Mathematical, machine, and deep learning-based CBVIR systems are introduced. Although the high computational cost of deep learning techniques remains the most efficient to utilize.

Similarity-based image browsing (original) (raw)

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