This paper presents optimized signal and image processing libraries from Intel Corporation. Intel Performance
Primitives (IPP) is a low-level signal and image processing library developed by Intel Corporation to optimize
code on Intel processors. Open Computer Vision library (OpenCV) is a high-level library dedicated to computer
vision tasks. This article describes the use of both libraries to build flexible and efficient signal and image processing applications.
Visual browsing is an important way of searching for images in large databases. In image retrieval, a lot
of problems have to be solved to get a good system: dimensionality curse, users' search context, size of the
database, visual features. In this article, a method trying to attenuate these problems is proposed. Each features
vector is organized into four signature vectors used in the classification process while building a fuzzy search tree
that is proposed to users for visual browsing. Our system gives good results in terms of speed and accuracy by
solving several problems of classical image retrieval methods.
This article presents a visual browsing content-based indexing and retrieval (CBIR) system for large image databases applied to a paleontology database. The studied system offers a hierarchical organization of feature vectors into signature vectors leading to a research tree so that users can explore the database visually. To build the tree, our technique consists in transforming the images using multiresolution analysis in order to extract features at multiple scales. Then a hierarchical signature vector for each scale is built using extracted features. An automatic classification of the obtained signatures is performed using the k-means algorithm. The images are grouped into clusters and for each cluster a model image is computed. This model image is inserted into a research tree proposed to users to browse the database visually. The process is reiterated and each cluster is split into sub-clusters with one model image per cluster, giving the nodes of the tree. The multiresolution approach combined with the organized signature vectors offers a coarse-to-fine research during the retrieval process (i.e. during the progression in the research tree).
In this article a research work in the field of content-based multiresolution indexing and retrieval of images is presented. Our method uses multiresolution decomposition of images using wavelets in the HSV colorspace to extract parameters at multiple scales allowing a progressive (coarse-to-fine) retrieval process. Features are automatically classified into several clusters with K-means algorithm. A model image is computed for each cluster in order to represent all the images of this cluster. The process is reiterated again and again and each cluster is sub-divided into sub-clusters. The model images are stored in a tree which is proposed to users for browsing the database. The nodes of the tree are the families and the leaves are the images of the database. A paleontology images database is used to test the proposed technique. This kind of approach permits to build a visual interface easy to use for users. Our main contribution is the building of the tree with multiresolution indexing and retrieval of images and the generation of model images to be proposed to users.
In this article a research work in the field of content- based image retrieval in large database applied to the Paleontology image database of the universite de Bourgogne, Dijon, France called 'TRANS TYFIPAL' is proposed. Our indexing method is based on multiresolution decomposition of database images using wavelets. For each kind of paleontology images we try to find a characteristic image representing it. This model image is computed using a classification algorithm on the space of parameters extracted from the wavelet transform of each image. Then a search tree is built to offer users a graphic interface for retrieving images. So that users have to navigate through this tree to find an image similar to that of their request. Our contribution in the field is the building of the model and of the search tree to make user access easier and faster. This paper ends with a conclusion on first coming results and a description of future work to be done to enhance our indexing and retrieval method.