![]() In this paper, we present a system that provides CBIR based on user-drawn sketches. Especially in collections where individual objects are not tagged with metadata describing their content, content-based image retrieval (CBIR) is a promising approach, but usually suffers from the unavailability of query images that are good enough to express the user's information need. With the increasingly growing size of digital image collections, known image search is gaining more and more importance. To illustrate the benefits of the approach, we show search results from the evaluation of QbS on the basis of the MIRFLICKR collection with 25'000 objects. The QbS system provides query support and offers several invariances that allow the user-generated sketch to slightly deviate from the searched image in terms of rotation, translation, relative size, and/or unknown objects in the background. By exploiting novel devices for human-computer interaction like interactive paper, tablet PCs, or graphic tablets, users are able to draw a sketch that reflects their information need and start a content-based search using this sketch. In order to improve this situation, we propose the QbS system which provides an approach to content-based search in large image collections based on user-drawn sketches. However, the application of CBIR to known item search usually suffers from the unavailability of query images that are good enough to express the user's information need. Especially when the objects in such collections do not possess appropriate metadata (e.g., tags, annotations), content-based image retrieval (CBIR) is a promising approach. Moreover, it reports on ongoing activities that aim at extending the system to support handwritten sketches, gestures and/or dynamic region selection to make the retrieval process more flexible and less dependent from existing query objects. The paper presents the integrated system which has already very successfully been applied to the development of an interactive museum catalogue. It is based on the iPaper/iServer system of ETH Zurich and the ISIS/OSIRIS content-based image retrieval system of the University of Basel. In this paper, we present a novel approach to query by sketch where interactive paper and image similarity search are seamlessly combined. Therefore, more flexible user interfaces are needed that allow users to sketch a query image by hand drawings and to dynamically select regions of interest from a given query image. If such reference images are not available or if the information need is covered only by parts of the query object, the result usually does not meet the user’s expectation. These reference images must be close to the final result so that the user can take them to express her information need. However, such queries require that one or even several reference images are available prior to the start of the search process. To illustrate the benefits of the QbS approach, we present search results from the evaluation of our system on the basis of the MIRFLICKR collection with 25,000 objects and compare the retrieval results of pure metadata-driven approaches, pure content-based retrieval using different sketches, and combinations thereof.Ĭontent-based retrieval has become a very popular and also powerful paradigm for searching in multimedia collections, especially in large collections of images. ![]() This combination offers several highly relevant invariances that allow the query sketch to slightly deviate from the searched image in terms of rotation, translation, relative size, and/or unknown objects in the background. The QbS system combines angular radial partitioning for the extraction of features in the user-provided sketch, taking into account the spatial distribution of edges, and the image distortion model. In this technical report, we present the QbS system that provides content-based search in large image collections based on user-drawn sketches. However, the application of CBIR to known item search usually suffers from the unavailability of query images that are good enough to express the user’s information need. Especially in collections where individual objects are not tagged with metadata describing their content, content-based image retrieval (CBIR) is a promising approach.
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