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Sana Fakhfakh

Other affiliations: University of Sfax
Bio: Sana Fakhfakh is an academic researcher from Salman bin Abdulaziz University. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 3, co-authored 11 publications receiving 14 citations. Previous affiliations of Sana Fakhfakh include University of Sfax.

Papers
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01 Jan 2014
TL;DR: A new method of search by con- tent based on Harris detector, Haar wavelet and Color histogram for the step of feature extraction to extract a binary signature for all multime- dia documents is presented.
Abstract: The Image search by content is an area that is based on a set of low-level features such as histograms, textures, the distribution of colors, shapes an brightness. The structure element is a shortcut of the image may vary depending on the designer of the XML document and which may change according to application needs. The visual ap- pearance of the image is a permanent factor that undergoes no change. Therefore, we present in this paper a new method of search by con- tent based on Harris detector, Haar wavelet and Color histogram for the step of feature extraction to extract a binary signature for all multime- dia documents.Finally We estimate the similarities between codes and search relevant images with Hamming distance.

4 citations

Journal ArticleDOI
TL;DR: In this paper, a visual vocabulary from phase extraction of descriptors such as color, texture, interest points is created from application of Haar wavelet multiscale, Harris interest points and analyzing color histogram.

3 citations

Proceedings Article
Hanen Karamti, Sana Fakhfakh1, Mohamed Tmar1, Walid Mahdi1, Faiez Gargouri1 
01 Jan 2014
TL;DR: This article describes the first participation to the multi- image plant observation queries task of PlantCLEF 2014 and presents two method, based on a modern technique for exploitation of structure of XML document, to identify plant species based on combination of textual and structural context of image.
Abstract: ImageCLEF 2014 has a challenge based on analysis for iden- tifying plants. This article describes our first participation to the multi- image plant observation queries task of PlantCLEF 2014. The task will be evaluated as a plant species retrieval task based on multi-image plant observations queries. The goal is to retrieve the correct plant species among the top results of a ranked list of species returned by the evalu- ated system. In this paper, we present two method. Our first method is purely visual and entirely automatic, using only the image information. One should mention that the total time spent with preparing this submission was only about three week. The results were accordingly fairly poor. The challenge of our second method is to identify plant species based on combination of textual and structural context of image. Indeed, we have used the meta-data in our system for exploring the image characteris- tics. Our approach is based on a modern technique for exploitation of structure of XML document. Also, the results were accordingly fairly poor. Although our results are not quite promising as compared to other par- ticipant groups, they can still guide our work in this field for some con- clusions reached.

3 citations

Journal ArticleDOI
TL;DR: A new scheme was made in this work that consists of calculating a three-dimensional head pose to capture a good iris image from a video sequence which affects the identification results.
Abstract: Current research in biometrics aims to develop high-performance tools, which would make it possible to better extract the traits specific to each individual and to grasp their discriminating characteristics. This research is based on high-level analyses of images, captured from the candidate to identify, for a better understanding and interpretation of these signals. Several biometric identification systems exist. The recognition systems based on the iris have many advantages and they are among the most reliable. In this paper, we propose a new approach based on biometric iris authentication. A new scheme was made in this work that consists of calculating a three-dimensional head pose to capture a good iris image from a video sequence which affects the identification results. From this image, we were able to locate the iris and analyse its texture by intelligent use of Meyer wavelets. Our approach was evaluated and approved through two databases CASIA Iris Distance and MiraclHB. The comparative study showed its effectiveness compared to those in the literature.

3 citations

Proceedings Article
01 Jan 2013
TL;DR: A new metric for multimedia retrieval in XML documents is proposed which is based on computing a geometric distance between XML nodes while taking into account kinship ties and proximities between them.
Abstract: Most documents available in Textual Database or in Internet are strongly structured. This is the case for example for scientific papers or written documents using markup languages (HTML, XML). This information provided by the structure can be exploited by systems of information retrieval to define the granularity of elements to return in response to a request made by a user or to improve the relevance of these results. In this article, We are interested in recovering multimedia elements. Like this, we propose a new metric for multimedia retrieval in XML documents which is based on computing a geometric distance between XML nodes while taking into account kinship ties and proximities between them. This measure will introduce a new source of evidence for multimedia retrieval in structural documents which aims at finding relevant multimedia element that focus on the user information need. Experiments have been undertaken to show the effectiveness of our method.

3 citations


Cited by
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Proceedings Article
15 Sep 2014
TL;DR: This overview presents more precisely the resources and assessments of task, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation results.
Abstract: The LifeCLEFs plant identication task provides a testbed for a system-oriented evaluation of plant identication about 500 species trees and herbaceous plants. Seven types of image content are considered: scan and scan-like pictures of leaf, and 6 kinds of detailed views with unconstrained conditions, directly photographed on the plant: ower, fruit, stem & bark, branch, leaf and entire view. The main originality of this data is that it was specically built through a citizen sciences initiative conducted by Tela Botanica, a French social network of amateur and expert botanists. This makes the task closer to the conditions of a realworld application. This overview presents more precisely the resources and assessments of task, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation results. With a total of ten groups from six countries and with a total of twenty seven submitted runs, involving distinct and original methods, this fourth year task confirms Image & Multimedia Retrieval community interest for biodiversity and botany, and highlights further challenging studies in plant identication

89 citations

Book ChapterDOI
15 Sep 2014
TL;DR: This paper presents the 2014 edition of LifeCLEF, i.e. the pilot one, and proposes to evaluate three tasks related to multimedia information retrieval and fine-grained classification problems in three living worlds based on large and real-world data.
Abstract: Using multimedia identification tools is considered as one of the most promising solutions to help bridging the taxonomic gap and build accurate knowledge of the identity, the geographic distribution and the evolution of living species. Large and structured communities of nature observers (e.g. eBird, Xeno-canto, Tela Botanica, etc.) as well as big monitoring equipments have actually started to produce outstanding collections of multimedia records. Unfortunately, the performance of the state-of-the-art analysis techniques on such data is still not well understood and is far from reaching the real world’s requirements. The LifeCLEF lab proposes to evaluate these challenges around three tasks related to multimedia information retrieval and fine-grained classification problems in three living worlds. Each task is based on large and real-world data and the measured challenges are defined in collaboration with biologists and environmental stakeholders in order to reflect realistic usage scenarios. This paper presents more particularly the 2014 edition of LifeCLEF, i.e. the pilot one. For each of the three tasks, we report the methodology and the datasets as well as the official results and the main outcomes.

81 citations

Journal ArticleDOI
TL;DR: A large-scale experiment aimed at evaluating how state-of-art computer vision systems perform in identifying plants compared to human expertise shows that the performances of automated plant identification systems are very promising and may open the door to a new generation of ecological surveillance systems.
Abstract: This paper reports a large-scale experiment aimed at evaluating how state-of-art computer vision systems perform in identifying plants compared to human expertise. A subset of the evaluation dataset used within LifeCLEF 2014 plant identification challenge was therefore shared with volunteers of diverse expertise, ranging from the leading experts of the targeted flora to inexperienced test subjects. In total, 16 human runs were collected and evaluated comparatively to the 27 machine-based runs of LifeCLEF challenge. One of the main outcomes of the experiment is that machines are still far from outperforming the best expert botanists at the image-based plant identification competition. On the other side, the best machine runs are competing with experienced botanists and clearly outperform beginners and inexperienced test subjects. This shows that the performances of automated plant identification systems are very promising and may open the door to a new generation of ecological surveillance systems.

21 citations

Journal ArticleDOI
TL;DR: In this paper, a visual vocabulary from phase extraction of descriptors such as color, texture, interest points is created from application of Haar wavelet multiscale, Harris interest points and analyzing color histogram.

3 citations

Proceedings Article
Hanen Karamti, Sana Fakhfakh1, Mohamed Tmar1, Walid Mahdi1, Faiez Gargouri1 
01 Jan 2014
TL;DR: This article describes the first participation to the multi- image plant observation queries task of PlantCLEF 2014 and presents two method, based on a modern technique for exploitation of structure of XML document, to identify plant species based on combination of textual and structural context of image.
Abstract: ImageCLEF 2014 has a challenge based on analysis for iden- tifying plants. This article describes our first participation to the multi- image plant observation queries task of PlantCLEF 2014. The task will be evaluated as a plant species retrieval task based on multi-image plant observations queries. The goal is to retrieve the correct plant species among the top results of a ranked list of species returned by the evalu- ated system. In this paper, we present two method. Our first method is purely visual and entirely automatic, using only the image information. One should mention that the total time spent with preparing this submission was only about three week. The results were accordingly fairly poor. The challenge of our second method is to identify plant species based on combination of textual and structural context of image. Indeed, we have used the meta-data in our system for exploring the image characteris- tics. Our approach is based on a modern technique for exploitation of structure of XML document. Also, the results were accordingly fairly poor. Although our results are not quite promising as compared to other par- ticipant groups, they can still guide our work in this field for some con- clusions reached.

3 citations