Image Browsing System Research Articles

Differential diagnosis of pelvic cystic lesions caused by hemorrhage from inflammatory abscess using CT attenuation in women with acute abdomen.

To determine the efficacy of computed tomography (CT) attenuation of cystic lesions measured on an image browsing system to distinguish abscess from hematoma in women with acute abdomen. The medical records of female patients of reproductive age with acute abdomen who were treated over a 7-year period in a single center and who had undergone laparotomy or laparoscopic surgery and preoperative pelvic CT scanning were retrospectively analyzed to identify those with hematoma or abscess cyst formation. Nineteen patients with tubo-ovarian abscess (abscess group) and six patients with hematoma (hematoma group) formation in the pelvis were included in the analysis. The preoperative CT images of the tubo-ovarian cyst were retrospectively investigated on the basis of cyst attenuation. CT attenuation of the cyst measured by both two gynecologists could be used to clearly distinguish inflammatory disease with abscess formation from bleeding disease with hematoma. CT attenuation on a picture archiving and communication system can distinguish hematoma from abscess in women with acute abdomen. This may significantly contribute to making differential diagnosis without interpretation by a medical radiologist.

Interactive browsing of image collections on mobile devices

Image collections are growing at a rapid rate and hence visual information is becoming more and more important. Clearly, these image repositories need to be managed, and tools for effectively and efficiently searching image databases are highly sought after, especially on mobile devices where more and more images are being stored. In this paper, we present an image browsing system for interactive exploration of image collections on mobile devices. Images are arranged so that visually similar images are grouped together while large image repositories become accessible through a hierarchical, browsable tree structure, arranged on a hexagonal lattice. The developed system provides an intuitive and fast interface for navigating through image databases using a variety of touch gestures.

Intuitive Image Database Browsing to Support Image Annotation

Image annotation is typically performed manually since automatic image annotation approaches have not matured yet to be used in practice. Consequently, image annotation is a labour intensive and time consuming task. In this paper, we show how an image browsing system can be employed to support efficient and effective (manual) annotation of image databases. In contrast to other approaches, which typically present images in a linear fashion, we employ a visualisation where images are arranged by mutual visual similarity. Since in this arrangement similar images are close to each other, they can easily be selected and annotated together. Organisation on a grid layout prevents image overlap and thus contributes to a clear presentation. Large image databases are handled through a hierarchical data structure where each image in the visualisation can correspond to a cluster of images that can be expanded by the user. Experimental results indicate that annotation can be performed faster on our proposed system.

Instance based personalized multi-form image browsing and retrieval

It is important to adapt and personalize image browsing and retrieval systems based on users' preferences for improved user experience and satisfaction. In this paper, we present a novel instance based personalized multi-form image representation with implicit relevance feedback and adaptive weighting approach for image browsing and retrieval systems. In the proposed system, images are grouped into forms, which represent different information on images such as location, content etc. We conducted user interviews on image browsing, sharing and retrieval systems for understanding image browsing and searching behaviors of users. Based on the insights gained from the user interview study we propose an adaptive weighting method and implicit relevance feedback for multi-form structures that aim to improve the efficiency and accuracy of the system. Statistics of the past actions are considered for modeling the target of the users. Thus, on each iteration weights of the forms are updated adaptively. Moreover, retrieval results are modified according to the users' preferences on iterations in order to improve personalized user experience. The proposed method has been evaluated and results are illustrated in the paper. It is shown that, satisfactory improvements can be achieved with proposed approaches in the multi-form scheme.

Intuitive Image Database Browsing to Support Image Annotation

Image annotation is typically performed manually since automatic image annotation approaches have not matured yet to be used in practice. Consequently, image annotation is a labour intensive and time consuming task. In this paper, we show how an image browsing system can be employed to support efficient and effective (manual) annotation of image databases. In contrast to other approaches, which typically present images in a linear fashion, we employ a visualisation where images are arranged by mutual visual similarity. Since in this arrangement similar images are close to each other, they can easily be selected and annotated together. Organisation on a grid layout prevents image overlap and thus contributes to a clear presentation. Large image databases are handled through a hierarchical data structure where each image in the visualisation can correspond to a cluster of images that can be expanded by the user. Experimental results indicate that annotation can be performed faster on our proposed system.

Utilizing quantitative users' reactions to represent affective meanings of an image

AbstractEmotional meaning is critical for users to retrieve relevant images. However, because emotional meanings are subject to the individual viewer's interpretation, they are considered difficult to implement when designing image retrieval systems. With the intent of making an image's emotional messages more readily accessible, this study aims to test a new approach designed to enhance the accessibility of emotional meanings during the image search process. This approach utilizes image searchers' emotional reactions, which are quantitatively measured. Broadly used quantitative measurements for emotional reactions, Semantic Differential (SD) and Self‐Assessment Manikin (SAM), were selected as tools for gathering users' reactions. Emotional representations obtained from these two tools were compared with three image perception tasks: searching, describing, and sorting. A survey questionnaire with a set of 12 images was administered to 58 participants, which were tagged with basic emotions. Results demonstrated that the SAM represents basic emotions on 2‐dimensional plots (pleasure and arousal dimensions), and this representation consistently corresponded to the three image perception tasks. This study provided experimental evidence that quantitative users' reactions can be a useful complementary element of current image retrieval/indexing systems. Integrating users' reactions obtained from the SAM into image browsing systems would reduce the efforts of human indexers as well as improve the effectiveness of image retrieval systems.

Adaptively browsing image databases with PIBE

Browsing large image collections is a complex and often tedious task, due to the semantic gap existing between the user subjective notion of similarity and the one according to which a browsing system organizes the images. In this paper we propose PIBE, an adaptive image browsing system, which provides users with a hierarchical view of images (the Browsing Tree) that can be customized according to user preferences. A key feature of PIBE is that it maintains local similarity criteria for each portion of the Browsing Tree. This makes it possible both to avoid costly global reorganization upon execution of user actions and, combined with a persistent storage of the Browsing Tree, to efficiently support multiple browsing tasks. We present the basic principles of PIBE and report experimental results showing the effectiveness of its browsing and personalization functionalities.

Embedded multilevel error diffusion

We present an algorithm for image browsing systems that embeds the output of binary Floyd-Steinberg (1975) error diffusion, or a low bit-depth gray-scale or color error diffused image into higher bit-depth gray-scale or color error diffused images. The benefits of this algorithm are that a low bit-depth halftoned image can be directly obtained from a higher bit-depth halftone for printing or progressive transmission simply by masking one or more bits off of the higher bit-depth image. The embedding can be done in any bits of the output, although the most significant or the least significant bits are most convenient. Due to constraints on the palette introduced by embedding, the image quality for the higher bit-depth halftone may be reduced. To preserve the image quality, we present algorithms for color palette organization, or binary index assignment, to be used as a preprocessing step to the embedding algorithm.