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Abstract
An image retrieval methodology suited for search in large collections of heterogeneous images is presented. The proposed approach employs a fully unsupervised segmentation algorithm to divide images into regions and endow the indexing and retrieval system with content-based functionalities. Low-level descriptors for the color, position, size, and shape of each region are subsequently extracted. These arithmetic descriptors are automatically associated with appropriate qualitative intermediate-level descriptors, which form a simple vocabulary termed object ontology. The object ontology is used to allow the qualitative definition of the high-level concepts the user queries for (semantic objects, each represented by a keyword) and their relations in a human-centered fashion. When querying for a specific semantic object (or objects), the intermediate-level descriptor values associated with both the semantic object and all image regions in the collection are initially compared, resulting in the rejection of most image regions as irrelevant. Following that, a relevance feedback mechanism, based on support vector machines and using the low-level descriptors, is invoked to rank the remaining potentially relevant image regions and produce the final query results. Experimental results and comparisons demonstrate, in practice, the effectiveness of our approach.
Highlights
In recent years, the accelerated growth of digital media collections and in particular still image collections, both proprietary and on the web, has established the need for the development of human-centered tools for the efficient access and retrieval of visual information
This endows the system with the capability to respond to anticipated queries without initially requiring any feedback; in a multiuser environment, it enables different users to share knowledge either in the form of semantic object descriptions or in the form of results retrieved from the database
Segmentation results are imperfect, as is generally the case with segmentation algorithms, most regions created by the proposed algorithm correspond to a semantic object or a part of one
Summary
The accelerated growth of digital media collections and in particular still image collections, both proprietary and on the web, has established the need for the development of human-centered tools for the efficient access and retrieval of visual information. Relatively simple and computationally efficient, this approach has several restrictions mainly deriving from the use of a restricted vocabulary that neither allows for unanticipated queries nor can be extended without reevaluating the possible connection between each image in the database and each new addition to the vocabulary Such keyword-based approaches assume either the preexistence of textual image annotations (e.g., captions) or that annotation, using the predetermined vocabulary, is performed manually. While such segmentation algorithms can endow an indexing and retrieval system with extensive content-based functionalities, these are limited by the main drawback of QbE approaches, that is, the need for the availability of an appropriate key image in order to start a query Satisfying this condition is not feasible, for image classes that are under-represented in the database.
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