In this page you can find details about several projects/softwares I’m involved, regarding Image Retrieval.
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The family of compact composite descriptors includes descriptors for 3 types of images. A group of 3 descriptors combines color and texture information in order to describe natural color images. In this page you can read more details about CCDs and download the related libraries. |
From this page you can download an open source implementation (in C#) of the Scalable Color Descriptor, Color Layout Descriptor, Dominant Colors Descriptor, as well as the Edge Histogram Descriptor. |
We employ the SURF detector to define salient image patches of blob-like textures and use the MPEG-7 Scalable Color (SC), Color Layout (CL), Edge Histogram (EH) and the Color and Edge Directivity Descriptor (CEDD) descriptors to produce the final local features’ vectors named SIMPLE-SC, SIMPLE-CL, SIMPLE-EH and SIMPLE-CEDD or “LoCATE” respectively. |
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In this web application, we present an experimental multimodal search engine, which allows multimedia and multi-language queries, and makes use of the total available information in a multimodal collection. All modalities are indexed and searched separately, and results can be fused with different methods. |
img(Rummager) brings into effect a number of new as well as state of the art descriptors. The application can execute an image search based on a query image, either from XML-based index files, or directly from a folder containing image files, extracting the comparison features in real time. In addition the img(Rummager) application can execute a hybrid search of images from the application server, combining keyword information and visual similarity. |
The LIRE (Lucene Image REtrieval) library provides a simple way to retrieve images and photos based on their color and texture characteristics. LIRE creates a Lucene index of image features for content based image retrieval (CBIR). Three of the available image features are taken from the MPEG-7 Standard: ScalableColor, ColorLayout and EdgeHistogram a fourth one, the Auto Color Correlogram has been implemented based on recent research results. |
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The application supports most of the standard measures used for evaluation in TREC, CLEF, and elsewhere, such as MAP, P10, P20, and bpref, as well as the Averaged Normalized Modified Retrieval Rank (ANMRR) proposed by MPEG for image retrieval evaluation. Additional features include a batch mode and statistical significance testing of the results against a pre-selected baseline. |
Traditional web image search was based only on the comparison between metadata of the webpage and the user’s textual description. Using TsoKaDo, images from various search engines are classified based on visual content and new tags are proposed to the user. Recursively, the results get closer to the user’s desire. |
Co.Vi.Wo.: Color Visual Words Based on Non-Predefined Size Codebooks (work in progress). |
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In this web-site a new set of feature descriptors is presented in a retrieval system. These descriptors have been designed with particular attention to their size and storage requirements, keeping them as small as possible without compromising their discriminating ability. These descriptors incorporate color and texture information into one histogram while keeping their sizes between 23 and 74 bytes per image. |