This paper proposes a new technique for auto-annotation and semantic retrieval based upon the idea of linearly
mapping an image feature space to a keyword space. The new technique is compared to several related techniques,
and a number of salient points about each of the techniques are discussed and contrasted. The paper also discusses
how these techniques might actually scale to a real-world retrieval problem, and demonstrates this though a
case study of a semantic retrieval technique being used on a real-world data-set (with a mix of annotated and
unannotated images) from a picture library.
The MapSnapper project aimed to develop a system for robust matching of low-quality images of a paper map taken from a mobile phone against a high quality digital raster representation of the same map. The paper presents a novel methodology for performing content-based image retrieval and object recognition from query images that have been degraded by noise and subjected to transformations through the imaging system. In addition the paper also provides an insight into the evaluation-driven development process that was used to incrementally improve the matching performance until the design specifications were met.
Users of image retrieval systems often find it frustrating that the image they are looking for is not ranked near
the top of the results they are presented. This paper presents a computational approach for ranking keyworded
images in order of relevance to a given keyword. Our approach uses machine learning to attempt to learn what
visual features within an image are most related to the keywords, and then provide ranking based on similarity
to a visual aggregate. To evaluate the technique, a Web 2.0 application has been developed to obtain a corpus
of user-generated ranking information for a given image collection that can be used to evaluate the performance
of the ranking algorithm.
This paper attempts to review and characterise the problem of the semantic gap in image retrieval and the attempts being made to bridge it. In particular, we draw from our own experience in user queries, automatic annotation and ontological techniques. The first section of the paper describes a characterisation of the semantic gap as a hierarchy between the raw media and full semantic understanding of the media's content. The second section discusses real users' queries with respect to the semantic gap. The final sections of the paper describe our own experience in attempting to bridge the semantic gap. In particular we discuss our work on auto-annotation and semantic-space models of image retrieval in order to bridge the gap from the bottom up, and the use of ontologies, which capture more semantics than keyword object labels alone, as a technique for bridging the gap from the top down.
Given the large amount of research into content-based image retrieval currently taking place, new interfaces to systems that perform queries based on image content need to be considered. A new paradigm for content-based image retrieval is introduced, in which a mobile device is used to capture the query image and display the results. The system consists of a client-server architecture in which query images are captured on a mobile device and then transferred to a server for further processing. The server then returns the results of the query to the mobile device. The use of a mobile device as an interface to a content-based image retrieval or object recognition system presents a number of challenges because the query image from the device will have been degraded by noise and subjected to transformations through the imaging system. A methodology is presented that uses techniques inspired from the information retrieval community in order to aid efficient indexing and retrieval. In particular, a vector-space model is used in the efficient indexing of each image, and a two-stage pruning/ranking procedure is used to determine the correct matching image. The retrieval algorithm is shown to outperform existing algorithms when used with query images from the device.