Fast indexing method for image retrieval using tree-structured lattices

Mejdoub, Mahmoud; Fonteles, Leonardo Hidd; Ben-Amar, Chokri; Antonini, Marc

doi:10.1109/cbmi.2008.4564970

Cited by 27 publications

(12 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In other side, Philippe and Matthieu [50] introduced in their paper the most known active learning methods for image retrieval such as Bayes classification, k-Nearest Neighbors [51], neural networks [52,53], wavelet network [54,55], lattice trees [56][57][58], Gaussian mixtures and support vector machines. Ekta and Hardeep [59] proposed the use of bayesian algorithm, as a supervised learning and a statistical method for classification, by reducing the noise from images.…”

Section: Low-level Content Approachesmentioning

confidence: 99%

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

ElAdel

Ejbali

Zaied

et al. 2016

Machine Vision and Applications

View full text Add to dashboard Cite

Content-based image retrieval (CBIR) has been a worthwhile topic for research for several years. A powerful CBIR system should minimize the semantic gap between the low-level features and high-level concepts in order to satisfy users requirements. Moreover, it should take into consideration the execution time. In this paper, we present a new semantic approach for CBIR supported by a parallel aggregation of content-based features extraction (shape, texture, color) using fuzzy support decision mechanisms. Shape features are based on Fast Beta Wavelet Network modeling and Hue moments. The texture descriptor is based on Energy computing at different decomposition levels. Finally, we present an implementation of a new color feature extraction based on fuzzy indexed color map. In the second stage, we propose a Fuzzy Decision Support System for feature (shape, texture, color) aggregation to improve the retrieval performance. The proposed approach is tested on four most popular datasets: Wang, INRIA Holidays, UKBench and samples from ImageNet, and the experiments showed that B Asma ElAdel the proposed approach can achieve a satisfactory retrieval performance with an acceptable search time.

show abstract

Section: Low-level Content Approachesmentioning

confidence: 99%

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

ElAdel

Ejbali

Zaied

et al. 2016

Machine Vision and Applications

View full text Add to dashboard Cite

show abstract

“…Having no previous knowledge about the location of the person in each video frame, the human action in a video stream can be recovered from a great number of local descriptors extracted from the video frames (Sekma et al, 2013), (Dammak et al, 2012), , (Sekma et al, 2014). Local descriptors, coupled with the bag-of-words (BOW) encoding method (Sivic and Zisserman, 2003) (Mejdoub et al, 2008) (Mejdoub et al, 2007) have recently become a very popular video representation (Ben Aoun et al, 2014), (Knopp et al, 2010), (Laptev et al, 2008), (Wang et al, 2009), (Alexander et al, 2008), (Wang et al, 2011), (Raptis and Soatto, 2010), (Pyry et al, 2010), (Jiang et al, 2012) and (Jain et al, 2013). The BOW uses a codebook to create a representation based on the visual content of a video, where the codebook is a set of visual words that represents the distribution of features of all the video.…”

Section: Intoductionmentioning

confidence: 99%

Human action recognition based on multi-layer Fisher vector encoding method

Sekma

Mejdoub

Amar

2015

Pattern Recognition Letters

Self Cite

View full text Add to dashboard Cite

“…To increase precision, we initialize k-means 8 times and keep the result with the lowest error. The BOW [20,21,22] representation then assigns each feature to the closest vocabulary word features. The resulting histograms of visual word occurrences are used as video sequence representations.…”

Section: Experimental Studymentioning

confidence: 99%

Spatio-temporal pyramidal accordion representation for human action recognition

Sekma

Mejdoub

Amar

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

View full text Add to dashboard Cite

We propose in this paper a spatio-temporal pyramid representation (STPR) of the video based Accordion image. The Accordion image allows the pixels having a high temporal correlation to be put in space adjacency. The STPR introduces spatial and temporal layout information to the local SIFT features computed on the Accordion image. It consists in applying firstly, a temporal pyramid decomposition on the video to divide it into a sequence of increasingly finer temporal blocks and secondly in performing a spatial pyramid representation on the Accordion images relative to the temporal blocks. The Multiple Kernel Learning approach is used to combine the multi-histograms coming from different SpatioTemporal Pyramid levels. Experiments using the human action recognition datasets (Hollywood2 and Olympic sports) show the effectiveness of the proposed approach.

show abstract

Fast indexing method for image retrieval using tree-structured lattices

Cited by 27 publications

References 9 publications

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

Human action recognition based on multi-layer Fisher vector encoding method

Spatio-temporal pyramidal accordion representation for human action recognition

Contact Info

Product

Resources

About