A New Approach for Video Concept Detection Based on User Comments

Video concept detection means describing a video with semantic concepts that correspond to the content of the video. The concepts help to retrieve video quickly. These semantic concepts describe high-level elements that depict the key information present in the content. In recent years, many efforts have been done to automate this task because the manual solution is time-consuming. Nowadays, videos come with comments. Therefore, in addition to the content of the videos, the comments should be analyzed because they contain valuable data that help to retrieve videos. This paper focused especially on videos shared on social media. The specificity of these videos was the presence of massive comments. This paper attempted to exploit comments by extracting concepts from them. This would support the research effort that works only on the visual content. Natural language processing techniques were used to analyze comments and to filter words to retain only the ones that could be considered as concepts. The proposed approach was tested on YouTube videos. The results demonstrated that the proposed approach was able to extract accurate data and concepts from the comments that could be used to ease the retrieval of videos. The findings supported the research effort of working on the visual and audio contents of the videos

Multiresolution Laplacian sparse coding technique for image representation

Sparse coding techniques have given good results in different domains especially in feature quantization and image representation. However, the major weakness of those techniques is their inability to represent the similarity between features. This limitation is due to the separate representation of features. Although the Laplacian sparse coding doesn’t focus on the spatial similarity in the image space, it preserves the locality of the features only in the data space. Due to this, the similarity between two local features belong to the similarity of their spatial neighborhood in the image. To overcome this flaw, we propose the integration of similarity based on Kullback-Leibler and wavelet decomposition in the domain of an image. This technique may surmount those limitations by taking into account each element of an image and its neighbors in similarity calculation. Classifications rates given by our approach show a clear improvement compared to those cited in this article

Urban Crowd Detection Using SOM, DBSCAN and LBSN Data Entropy: A Twitter Experiment in New York and Madrid

The surfer and the physical location are two important concepts associated with each other in the social network-based localization service. This work consists of studying urban behavior based on location-based social networks (LBSN) data; we focus especially on the detection of abnormal events. The proposed crowd detection system uses the geolocated social network provided by the Twitter application programming interface (API) to automatically detect the abnormal events. The methodology we propose consists of using an unsupervised competitive learning algorithm (self-organizing map (SOM)) and a density-based clustering method (density-based spatial clustering of applications with noise (DBCSAN)) to identify and detect crowds. The second stage is to build the entropy model to determine whether the detected crowds fit into the daily pattern with reference to a spatio-temporal entropy model, or whether they should be considered as evidence that something unusual occurs in the city because of their number, size, location and time of day. To detect an abnormal event in the city, it is sufficient to determine the real entropy model and to compare it with the reference model. For the normal day, the reference model is constructed offline for each time interval. The obtained results confirm the effectiveness of our method used in the first stage (SOM and DBSCAN stage) to detect and identify clusters dynamically, and imitating human activity. These findings also clearly confirm the detection of special days in New York City (NYC), which proves the performance of our proposed model

Delayed payment of residential water invoice and sustainability of water demand management

This paper investigates empirically the reasons for delays in the payment of clean water invoice in Algeria. Using a data set of 27,363 households connected to the water services network and a small-sample survey of 172 household, we estimate several duration models to better understand the main determinants of water invoice time to payment. The delayed payment of water bills could be explained by three determinants: the household financial constraints, the quality of the public service provided, and disincentives through increasing water tariff structure, which is used to manage sustainably the demand for water. This study calls for a modification in the tariff structure to promote equity and water resource protection. It also suggests providing additional efforts to improve the quality of the public water service offered

Delayed payment of residential water invoice and sustainability of water demand management

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