MASCOT: a mechanism for attention-based scale-invariant object recognition in images

The efficient management of large multimedia databases requires the development of new techniques to process, characterize, and search for multimedia objects. Especially in the case of image data, the rapidly growing amount of documents prohibits a manual description of the images’ content. Instead, the automated characterization is highly desirable to support annotation and retrieval of digital images. However, this is a very complex and still unsolved task. To contribute to a solution of this problem, we have developed a mechanism for recognizing objects in images based on the query by example paradigm. Therefore, the most salient image features of an example image representing the searched object are extracted to obtain a scale-invariant object model. The use of this model provides an efficient and robust strategy for recognizing objects in images independently of their size. Further applications of the mechanism are classical recognition tasks such as scene decomposition or object tracking in video sequences

Video spatio-temporal filtering based on cameras and target objects trajectories - Videosurveillance forensic framework

International audienceThis paper presents our work about assisting video-surveillance agents in the search for particular video scenes of interest in transit network. This work has been developed based on requirements defined within different projects with the French National Police in a forensic goal. The video-surveillance agent inputs a query in the form of a hybrid trajectory (date, time, locations expressed with regards to different reference systems) and potentially some visual descriptions of the scene. The query processing starts with the interpretation of the hybrid trajectory and continues with a selection of a set of cameras likely to have filmed the spatial trajectory. The main contributions of this paper are: (1) a definition of the hybrid trajectory query concept, trajectory that is constituted of geometrical and symbolic segments represented with regards to different reference systems (e.g., Geodesic system, road network), (2) a spatio-temporal filtering framework based on a spatio-temporal modeling of the transit network and associated cameras

Structured Knowledge Representation for Image Retrieval

We propose a structured approach to the problem of retrieval of images by content and present a description logic that has been devised for the semantic indexing and retrieval of images containing complex objects. As other approaches do, we start from low-level features extracted with image analysis to detect and characterize regions in an image. However, in contrast with feature-based approaches, we provide a syntax to describe segmented regions as basic objects and complex objects as compositions of basic ones. Then we introduce a companion extensional semantics for defining reasoning services, such as retrieval, classification, and subsumption. These services can be used for both exact and approximate matching, using similarity measures. Using our logical approach as a formal specification, we implemented a complete client-server image retrieval system, which allows a user to pose both queries by sketch and queries by example. A set of experiments has been carried out on a testbed of images to assess the retrieval capabilities of the system in comparison with expert users ranking. Results are presented adopting a well-established measure of quality borrowed from textual information retrieval

Shape annotation for intelligent image retrieval

Annotation of shapes is an important process for semantic image retrieval. In this paper, we present a shape annotation framework that enables intelligent image retrieval by exploiting in a unified manner domain knowledge and perceptual description of shapes. A semi-supervised fuzzy clustering process is used to derive domain knowledge in terms of linguistic concepts referring to the semantic categories of shapes. For each category we derive a prototype that is a visual template for the category. A novel visual ontology is proposed to provide a description of prototypes and their salient parts. To describe parts of prototypes the visual ontology includes perceptual attributes that are defined by mimicking the analogy mechanism adopted by humans to describe the appearance of objects. The effectiveness of the developed framework as a facility for intelligent image retrieval is shown through results on a case study in the domain of fish shapes

Spatio-temporal metadata filtering and synchronising invideo surveillance

International audienceThis paper presents an ongoing work that aims at assisting videoprotection agents in the search for particular video scenes of interest in transit network. The video-protection agent inputs a query in the form of date, time, location and a visual description of the scene. The query processing starts by selecting a set of cameras likely to have filmed the scene followed by an analysis of the video content obtained from these cameras. The main contribution of this paper is the innovative framework that is composed of: (1) a spatiotemporal filtering method based on a spatio-temporal modeling of the transit network and associated cameras, and (2)a content-based retrieval based method on visual features. The presented filtering framework is to be tested on real data acquired within a French National project in partnership with the French Interior Ministry and the French National Police. The project aims at setting up public demonstrators that will be used by researchers and commercials from the video-protection community