CONTENT BASED IMAGE RETRIEVAL (CBIR) SYSTEM

Advancement in hardware and telecommunication technology has boosted up creation and distribution of digital visual content. However this rapid growth of visual content creations has not been matched by the simultaneous emergence of technologies to support efficient image analysis and retrieval. Although there are attempt to solve this problem by using meta-data text annotation but this approach are not practical when it come to the large number of data collection. This system used 7 different feature vectors that are focusing on 3 main low level feature groups (color, shape and texture). This system will use the image that the user feed and search the similar images in the database that had similar feature by considering the threshold value. One of the most important aspects in CBIR is to determine the correct threshold value. Setting the correct threshold value is important in CBIR because setting it too low will result in less image being retrieve that might exclude relevant data. Setting to high threshold value might result in irrelevant data to be retrieved and increase the search time for image retrieval. Result show that this project able to increase the image accuracy to average 70% by combining 7 different feature vector at correct threshold value. ii

Colour-based image retrieval algorithms based on compact colour descriptors and dominant colour-based indexing methods

Content based image retrieval (CBIR) is reported as one of the most active research areas in the last two decades, but it is still young. Three CBIR’s performance problem in this study is inaccuracy of image retrieval, high complexity of feature extraction, and degradation of image retrieval after database indexing. This situation led to discrepancies to be applied on limited-resources devices (such as mobile devices). Therefore, the main objective of this thesis is to improve performance of CBIR. Images’ Dominant Colours (DCs) is selected as the key contributor for this purpose due to its compact property and its compatibility with the human visual system. Semantic image retrieval is proposed to solve retrieval inaccuracy problem by concentrating on the images’ objects. The effect of image background is reduced to provide more focus on the object by setting weights to the object and the background DCs. The accuracy improvement ratio is raised up to 50% over the compared methods. Weighting DCs framework is proposed to generalize this technique where it is demonstrated by applying it on many colour descriptors. For reducing high complexity of colour Correlogram in terms of computations and memory space, compact representation of Correlogram is proposed. Additionally, similarity measure of an existing DC-based Correlogram is adapted to improve its accuracy. Both methods are incorporated to produce promising colour descriptor in terms of time and memory space complexity. As a result, the accuracy is increased up to 30% over the existing methods and the memory space is decreased to less than 10% of its original space. Converting the abundance of colours into a few DCs framework is proposed to generalize DCs concept. In addition, two DC-based indexing techniques are proposed to overcome time problem, by using RGB and perceptual LUV colour spaces. Both methods reduce the search space to less than 25% of the database size with preserving the same accuracy

PhotoScout: Synthesis-Powered Multi-Modal Image Search

Due to the availability of increasingly large amounts of visual data, there is a growing need for tools that can help users find relevant images. While existing tools can perform image retrieval based on similarity or metadata, they fall short in scenarios that necessitate semantic reasoning about the content of the image. This paper explores a new multi-modal image search approach that allows users to conveniently specify and perform semantic image search tasks. With our tool, PhotoScout, the user interactively provides natural language descriptions, positive and negative examples, and object tags to specify their search tasks. Under the hood, PhotoScout is powered by a program synthesis engine that generates visual queries in a domain-specific language and executes the synthesized program to retrieve the desired images. In a study with 25 participants, we observed that PhotoScout allows users to perform image retrieval tasks more accurately and with less manual effort

CONTENT BASED IMAGE RETRIEVAL (CBIR) SYSTEM

Advancement in hardware and telecommunication technology has boosted up creation and distribution of digital visual content. However this rapid growth of visual content creations has not been matched by the simultaneous emergence of technologies to support efficient image analysis and retrieval. Although there are attempt to solve this problem by using meta-data text annotation but this approach are not practical when it come to the large number of data collection. This system used 7 different feature vectors that are focusing on 3 main low level feature groups (color, shape and texture). This system will use the image that the user feed and search the similar images in the database that had similar feature by considering the threshold value. One of the most important aspects in CBIR is to determine the correct threshold value. Setting the correct threshold value is important in CBIR because setting it too low will result in less image being retrieve that might exclude relevant data. Setting to high threshold value might result in irrelevant data to be retrieved and increase the search time for image retrieval. Result show that this project able to increase the image accuracy to average 70% by combining 7 different feature vector at correct threshold value. ii

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges