Dublin City University at CLEF 2004: experiments with the ImageCLEF St Andrew's collection

For the CLEF 2004 ImageCLEF St Andrew's Collection task the Dublin City University group carried out three sets of experiments: standard cross-language information retrieval (CLIR) runs using topic translation via machine translation (MT), combination of this run with image matching results from the VIPER system, and a novel document rescoring approach based on automatic MT evaluation metrics. Our standard MT-based CLIR works well on this task. Encouragingly combination with image matching lists is also observed to produce small positive changes in the retrieval output. However, rescoring using the MT evaluation metrics in their current form significantly reduced retrieval effectiveness

Automatically organising images using concept hierarchies

In this paper we discuss the use of concept hierarchies, an approach to automatically organize a set of documents based upon a set of concepts derived from the documents themselves for image retrieval. Co-occurrence between terms associated with image captions and a statistical relation called subsumption are used to generate term clusters which are organized hierarchically. Previously, the approach has been studied for document retrieval and results have shown that automatically generating hierarchies can help users with their search task. In this paper we present an implementation of concept hierarchies for image retrieval, together with preliminary ad-hoc evaluation. Although our approach requires more investigation, initial results from a prototype system are promising and would appear to provide a useful summary of the search results

Automatic organisation of retrieved images into a hierarchy

Image retrieval is of growing interest to both search engines and academic researchers with increased focus on both content-based and caption-based approaches. Image search, however, is different from document retrieval: users often search a broader set of retrieved images than they would examine returned web pages in a search engine. In this paper, we focus on a concept hierarchy generation approach developed by Sanderson and Croft in 1999, which was used to organise retrieved images in a hierarchy automatically generated from image captions. Thirty participants were recruited for the study. Each of them conducted two different kinds of searching tasks within the system. Results indicated that the user retrieval performance in both interfaces of system is similar. However, the majority of users preferred to use the concept hierarchy to complete their searching tasks and they were satisfied with using the hierarchical menu to organize retrieved results, because the menu appeared to provide a useful summary to help users look through the image results

Creating a test collection to evaluate diversity in image retrieval

This paper describes the adaptation of an existing test collection for image retrieval to enable diversity in the results set to be measured. Previous research has shown that a more diverse set of results often satisfies the needs of more users better than standard document rankings. To enable diversity to be quantified, it is necessary to classify images relevant to a given theme to one or more sub-topics or clusters. We describe the challenges in building (as far as we are aware) the first test collection for evaluating diversity in image retrieval. This includes selecting appropriate topics, creating sub-topics, and quantifying the overall effectiveness of a retrieval system. A total of 39 topics were augmented for cluster-based relevance and we also provide an initial analysis of assessor agreement for grouping relevant images into sub-topics or clusters

Photograph indexing and retrieval using star-graphs

International audienceWe present in this paper a relational approach for indexing and retrieving photographs from a collection. Instead of using simple keywords as an indexing language, we propose to use star-graphs as document descriptors. A star-graph is a conceptual graph that contains a single relation, with some concepts linked to it. They are elementary pieces of information describing combinations of concepts. We use star-graphs as descriptors - or index terms - for image content representation. This allows for relational indexing and expression of complex user needs, in comparison to classical text retrieval, where simple keywords are generally used as document descriptors. We present a document representation model, a weighting scheme for star-graphs inspired by the tf.idf used in text retrieval. We have applied our model to image retrieval, and show the system evaluation results

Evolution of Information Retrieval System: Critical Review of Multimedia Information Retrieval System Based On Content, Context, and Concept

In recent years the explosive growth of information affects the flood of information. The amount of information must be followed by the development of the effective Information Retrieval System (IRS) so that the information will be easily accessible and useful for the user. The source of Information contains various media format, beside text there is also image, audio, and video that called multimedia. A large number of multimedia information rise the Multimedia Information Retrieval System (MIRS). Most of MIRS today is monolithic or only using one media format like Google1 for text search, tineye2 for image search, youtube3 for video search or 4shared4 for music and audio search. There is a need of information in any kind of media, not only retrieve the document in text format, but also retrieve the document in an image, audio and video format at once from any kind media format of the query. This study reviews the evolution of IRS, regress from text-based to concept- based MIRS. Unified Multimedia Indexing technique is discussed along with Concept-based MIRS. This critical review concludes that the evolution of IRS follows three paces: content-based, context-based and concept-based. Each pace takes on indexing system and retrieval techniques to optimize information retrieved. The challenge is how to come up with a retrieval technique that can process unified MIRS in order to retrieve optimally the relevant document

Unsupervised Graph-based Rank Aggregation for Improved Retrieval

This paper presents a robust and comprehensive graph-based rank aggregation approach, used to combine results of isolated ranker models in retrieval tasks. The method follows an unsupervised scheme, which is independent of how the isolated ranks are formulated. Our approach is able to combine arbitrary models, defined in terms of different ranking criteria, such as those based on textual, image or hybrid content representations. We reformulate the ad-hoc retrieval problem as a document retrieval based on fusion graphs, which we propose as a new unified representation model capable of merging multiple ranks and expressing inter-relationships of retrieval results automatically. By doing so, we claim that the retrieval system can benefit from learning the manifold structure of datasets, thus leading to more effective results. Another contribution is that our graph-based aggregation formulation, unlike existing approaches, allows for encapsulating contextual information encoded from multiple ranks, which can be directly used for ranking, without further computations and post-processing steps over the graphs. Based on the graphs, a novel similarity retrieval score is formulated using an efficient computation of minimum common subgraphs. Finally, another benefit over existing approaches is the absence of hyperparameters. A comprehensive experimental evaluation was conducted considering diverse well-known public datasets, composed of textual, image, and multimodal documents. Performed experiments demonstrate that our method reaches top performance, yielding better effectiveness scores than state-of-the-art baseline methods and promoting large gains over the rankers being fused, thus demonstrating the successful capability of the proposal in representing queries based on a unified graph-based model of rank fusions

Content-based indexing of low resolution documents

In any multimedia presentation, the trend for attendees taking pictures of slides that interest them during the presentation using capturing devices is gaining popularity. To enhance the image usefulness, the images captured could be linked to image or video database. The database can be used for the purpose of file archiving, teaching and learning, research and knowledge management, which concern image search. However, the above-mentioned devices include cameras or mobiles phones have low resolution resulted from poor lighting and noise. Content-Based Image Retrieval (CBIR) is considered among the most interesting and promising fields as far as image search is concerned. Image search is related with finding images that are similar for the known query image found in a given image database. This thesis concerns with the methods used for the purpose of identifying documents that are captured using image capturing devices. In addition, the thesis also concerns with a technique that can be used to retrieve images from an indexed image database. Both concerns above apply digital image processing technique. To build an indexed structure for fast and high quality content-based retrieval of an image, some existing representative signatures and the key indexes used have been revised. The retrieval performance is very much relying on how the indexing is done. The retrieval approaches that are currently in existence including making use of shape, colour and texture features. Putting into consideration these features relative to individual databases, the majority of retrievals approaches have poor results on low resolution documents, consuming a lot of time and in the some cases, for the given query image, irrelevant images are obtained. The proposed identification and indexing method in the thesis uses a Visual Signature (VS). VS consists of the captures slides textual layout’s graphical information, shape’s moment and spatial distribution of colour. This approach, which is signature-based are considered for fast and efficient matching to fulfil the needs of real-time applications. The approach also has the capability to overcome the problem low resolution document such as noisy image, the environment’s varying lighting conditions and complex backgrounds. We present hierarchy indexing techniques, whose foundation are tree and clustering. K-means clustering are used for visual features like colour since their spatial distribution give a good image’s global information. Tree indexing for extracted layout and shape features are structured hierarchically and Euclidean distance is used to get similarity image for CBIR. The assessment of the proposed indexing scheme is conducted based on recall and precision, a standard CBIR retrieval performance evaluation. We develop CBIR system and conduct various retrieval experiments with the fundamental aim of comparing the accuracy during image retrieval. A new algorithm that can be used with integrated visual signatures, especially in late fusion query was introduced. The algorithm has the capability of reducing any shortcoming associated with normalisation in initial fusion technique. Slides from conferences, lectures and meetings presentation are used for comparing the proposed technique’s performances with that of the existing approaches with the help of real data. This finding of the thesis presents exciting possibilities as the CBIR systems is able to produce high quality result even for a query, which uses low resolution documents. In the future, the utilization of multimodal signatures, relevance feedback and artificial intelligence technique are recommended to be used in CBIR system to further enhance the performance

Exploiting multimedia content : a machine learning based approach

Advisors: Prof. M Gopal, Prof. Santanu Chaudhury. Date and location of PhD thesis defense: 10 September 2013, Indian Institute of Technology DelhiThis thesis explores use of machine learning for multimedia content management involving single/multiple features, modalities and concepts. We introduce shape based feature for binary patterns and apply it for recognition and retrieval application in single and multiple feature based architecture. The multiple feature based recognition and retrieval frameworks are based on the theory of multiple kernel learning (MKL). A binary pattern recognition framework is presented by combining the binary MKL classifiers using a decision directed acyclic graph. The evaluation is shown for Indian script character recognition, and MPEG7 shape symbol recognition. A word image based document indexing framework is presented using the distance based hashing (DBH) defined on learned pivot centres. We use a new multi-kernel learning scheme using a Genetic Algorithm for developing a kernel DBH based document image retrieval system. The experimental evaluation is presented on document collections of Devanagari, Bengali and English scripts. Next, methods for document retrieval using multi-modal information fusion are presented. Text/Graphics segmentation framework is presented for documents having a complex layout. We present a novel multi-modal document retrieval framework using the segmented regions. The approach is evaluated on English magazine pages. A document script identification framework is presented using decision level aggregation of page, paragraph and word level prediction. Latent Dirichlet Allocation based topic modelling with modified edit distance is introduced for the retrieval of documents having recognition inaccuracies. A multi-modal indexing framework for such documents is presented by a learning based combination of text and image based properties. Experimental results are shown on Devanagari script documents. Finally, we have investigated concept based approaches for multimedia analysis. A multi-modal document retrieval framework is presented by combining the generative and discriminative modelling for exploiting the cross-modal correlation between modalities. The combination is also explored for semantic concept recognition using multi-modal components of the same document, and different documents over a collection. An experimental evaluation of the framework is shown for semantic event detection in sport videos, and semantic labelling of components of multi-modal document images