The rapid growth of image digitization and collections in recent years makes it challenging and burdensome to organize, categorize, and retrieve similar images from voluminous collections. Content-based image retrieval (CBIR) is immensely convenient in this context. A considerable number of local feature detectors and descriptors are present in the literature of CBIR. We propose a model to anticipate the best feature combinations for image retrieval-related applications. Several spatial complementarity criteria of local feature detectors are analyzed and then engaged in a regression framework to find the optimal combination of detectors for a given dataset and are better adapted for each given image; the proposed model is also useful to optimally fix some other parameters, such as the k in k-nearest neighbor retrieval. Three public datasets of various contents and sizes are employed to evaluate the proposal, which is legitimized by improving the quality of retrieval notably facing classical approaches. Finally, the proposed image search engine is applied to the cultural photographic collections of a French museum, where it demonstrates its added value for the exploration and promotion of these contents at different levels from their archiving up to their exhibition in or ex situ.
KEYWORDS: Optical spheres, Image retrieval, Databases, Feature extraction, Content based image retrieval, Multimedia, Data mining, Sensors, 3D displays, Decision support systems
In this paper, we are interested in the fast retrieval, in a large collection of points in high-dimensional space, of points close to a set of m query points (a multiple query): we want to efficiently find the sequence Ai,iε1,m} where Ai is the set of points within a sphere of center query point pi,iε{1,m} and radius ε (a sphere query). It has been argued that beyond a rather small dimension (d ⩾ 10) for such sphere queries as well as for other similarity queries, sequentially scanning the collection of points is faster than crossing a tree structure indexing the collection (the so-called curse of dimensionality phenomenon). Our first contribution is to experimentally assess whether the curse of dimensionality is reached with various points distributions. We compare the performance of a single sphere query when the collection is indexed by a tree structure (an SR-tree in our experiments) to that of a sequential scan. The second objective of this paper is to propose and evaluate several algorithms for multiple queries in a collection of points indexed by a tree structure. We compare the performance of these algorithms to that of a naive one consisting in sequentially running the m queries. This study is applied to content-based image retrieval where images are described by local descriptors based on points of interest. Such descriptors involve a relatively small dimension (8 to 30) justifying that the collection of points be indexed by a tree structure; similarity search with local descriptors implies multiple sphere queries that are usually time expensive, justifying the proposal of new strategies.
In this paper, we present our work for automatic generation of textual metadata based on visual content analysis of video news. We present two methods for semantic object detection and recognition from a cross modal image-text thesaurus. These thesaurus represent a supervised association between models and semantic labels. This paper is concerned with two semantic objects: faces and Tv logos. In the first part, we present our work for efficient face detection and recogniton with automatic name generation. This method allows us also to suggest the textual annotation of shots close-up estimation. On the other hand, we were interested to automatically detect and recognize different Tv logos present on incoming different news from different Tv Channels. This work was done jointly with the French Tv
Channel TF1 within the "MediaWorks" project that consists on an hybrid text-image indexing and retrieval plateform for video news.
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