Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Hybrid image representation methods for automatic image annotation: a survey

In most automatic image annotation systems, images are represented with low level features using either global methods or local methods. In global methods, the entire image is used as a unit. Local methods divide images into blocks where fixed-size sub-image blocks are adopted as sub-units; or into regions by using segmented regions as sub-units in images. In contrast to typical automatic image annotation methods that use either global or local features exclusively, several recent methods have considered incorporating the two kinds of information, and believe that the combination of the two levels of features is beneficial in annotating images. In this paper, we provide a survey on automatic image annotation techniques according to one aspect: feature extraction, and, in order to complement existing surveys in literature, we focus on the emerging image annotation methods: hybrid methods that combine both global and local features for image representation

Efficient video indexing for monitoring disease activity and progression in the upper gastrointestinal tract

Endoscopy is a routine imaging technique used for both diagnosis and minimally invasive surgical treatment. While the endoscopy video contains a wealth of information, tools to capture this information for the purpose of clinical reporting are rather poor. In date, endoscopists do not have any access to tools that enable them to browse the video data in an efficient and user friendly manner. Fast and reliable video retrieval methods could for example, allow them to review data from previous exams and therefore improve their ability to monitor disease progression. Deep learning provides new avenues of compressing and indexing video in an extremely efficient manner. In this study, we propose to use an autoencoder for efficient video compression and fast retrieval of video images. To boost the accuracy of video image retrieval and to address data variability like multi-modality and view-point changes, we propose the integration of a Siamese network. We demonstrate that our approach is competitive in retrieving images from 3 large scale videos of 3 different patients obtained against the query samples of their previous diagnosis. Quantitative validation shows that the combined approach yield an overall improvement of 5% and 8% over classical and variational autoencoders, respectively.Comment: Accepted at IEEE International Symposium on Biomedical Imaging (ISBI), 201