Learning a Complete Image Indexing Pipeline

To work at scale, a complete image indexing system comprises two components: An inverted file index to restrict the actual search to only a subset that should contain most of the items relevant to the query; An approximate distance computation mechanism to rapidly scan these lists. While supervised deep learning has recently enabled improvements to the latter, the former continues to be based on unsupervised clustering in the literature. In this work, we propose a first system that learns both components within a unifying neural framework of structured binary encoding

Learning a Complete Image Indexing Pipeline

To work at scale, a complete image indexing system comprises two components: An inverted file index to restrict the actual search to only a subset that should contain most of the items relevant to the query; An approximate distance computation mechanism to rapidly scan these lists. While supervised deep learning has recently enabled improvements to the latter, the former continues to be based on unsupervised clustering in the literature. In this work, we propose a first system that learns both components within a unifying neural framework of structured binary encoding

Intelligent indexing of crime scene photographs

The Scene of Crime Information System's automatic image-indexing prototype goes beyond extracting keywords and syntactic relations from captions. The semantic information it gathers gives investigators an intuitive, accurate way to search a database of cases for specific photographic evidence. Intelligent, automatic indexing and retrieval of crime scene photographs is one of the main functions of SOCIS, our research prototype developed within the Scene of Crime Information System project. The prototype, now in its final development and evaluation phase, applies advanced natural language processing techniques to text-based image indexing and retrieval to tackle crime investigation needs effectively and efficiently

Image Indexing and Retrieval

The amount of pictorial data has been growing enormously with the expansion of WWW. From the large number of images, it is very important for users to retrieve required images via an efficient and effective mechanism. To solve the image retrieval problem, many techniques have been devised addressing the requirement of different applications. Problem of the traditional methods of image indexing have led to the rise of interest in techniques for retrieving images on the basis of automatically derived features such as color, texture and shape… a technology generally referred as Content-Based Image Retrieval (CBIR). After decade of intensive research, CBIR technology is now beginning to move out of the laboratory into the marketplace. However, the technology still lacks maturity and is not yet being used in a significant scale

Context-based conceptual image indexing

International audienceAutomatic semantic classification of image databases is very useful for users searching and browsing, but it is at the same time a very challenging research problem as well. Local features based image classification is one of the key issues to bridge the semantic gap in order to detect concepts. This paper proposes a framework for incorporating contextual information into the concept detection process. The proposed method combines local and global classifiers with stacking, using SVM.We studied the impact of topologic and semantic contexts in concept detection performance and proposed solutions to handle the large amount of dimensions involved in classified data. We conducted experiments on TRECVID�04 subset with 48104 images and 5 concepts. We found that the use of context yields a significant improvement both for the topologic and semantic contexts

Interindexer Consistency, Term Usage, and Indexer Experience Levels in the Application of Image Descriptors

This study concerns image indexing and the affect of indexer experience levels on interindexer consistency and the choice of indexing terms. Owing to the importance of concept-based indexing for images, this investigation will provide information for the development of basic criteria for image indexing practices. Four groups of participants with varying degrees of image indexing and subject expertise will be studied through an interactive Web site. A questionnaire will gather information on indexer experience levels and basic demographic data, and an image component of the study will gather indexing terms applied by the participants. Quantitative analysis will be conducted on the data resulting from the questionnaire, while qualitative methods will be employed for analyzing the indexing terms assigned by the participants. The study will examine the multiplicity of term types applied to images (generic description, identification, and interpretation) and the degree of indexing difficulty due to the accessibility of representation and subject content of the image. It is hoped that this study will lead to a deeper understanding of the role of indexer experience in image indexing, which in turn can inform the processes utilized to enhance access to digital collections of visual materials

VisualNet: Commonsense knowledgebase for video and image indexing and retrieval application

The rapidly increasing amount of video collections, available on the web or via broadcasting, motivated research towards building intelligent tools for searching, rating, indexing and retrieval purposes. Establishing a semantic representation of visual data, mainly in textual form, is one of the important tasks. The time needed for building and maintaining Ontologies and knowledge, especially for wide domain, and the efforts for integrating several approaches emphasize the need for unified generic commonsense knowledgebase for visual applications. In this paper, we propose a novel commonsense knowledgebase that forms the link between the visual world and its semantic textual representation. We refer to it as "VisualNet". VisualNet is obtained by our fully automated engine that constructs a new unified structure concluding the knowledge from two commonsense knowledgebases, namely WordNet and ConceptNet. This knowledge is extracted by performing analysis operations on WordNet and ConceptNet contents, and then only useful knowledge in visual domain applications is considered. Moreover, this automatic engine enables this knowledgebase to be developed, updated and maintained automatically, synchronized with any future enhancement on WordNet or ConceptNet. Statistical properties of the proposed knowledgebase, in addition to an evaluation of a sample application results, show coherency and effectiveness of the proposed knowledgebase and its automatic engine

Bag-of-Features Image Indexing and Classification in Microsoft SQL Server Relational Database

This paper presents a novel relational database architecture aimed to visual objects classification and retrieval. The framework is based on the bag-of-features image representation model combined with the Support Vector Machine classification and is integrated in a Microsoft SQL Server database.Comment: 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), Gdynia, Poland, 24-26 June 201