Pub Date : 2018-09-01DOI: 10.6025/jmpt/2018/9/3/88-101
Rabab Beniani, K. Faraoun
In this paper, we propose a new scheme for joint compression and encryption of digital images, based on cellular automata and selective encryption of quantized DCT coefficient. Using a key stream by a cellular automata mechanism, a subset of quantized coefficients is selected and then ciphered with the produced pseudorandom key-stream. Thus, we achieve a sufficiently robust security level, mostly against known plaintext attacks while preserving a high compression ratio. Several performance analysis including security analysis, speed performances and compression ratio are performed, and demonstrate the efficacy of the proposed approach with respect to existing ones.
{"title":"A Mixed Chaotic-cellular Automata Based Encryption Scheme for Compressed Jpeg Images","authors":"Rabab Beniani, K. Faraoun","doi":"10.6025/jmpt/2018/9/3/88-101","DOIUrl":"https://doi.org/10.6025/jmpt/2018/9/3/88-101","url":null,"abstract":"In this paper, we propose a new scheme for joint compression and encryption of digital images, based on cellular automata and selective encryption of quantized DCT coefficient. Using a key stream by a cellular automata mechanism, a subset of quantized coefficients is selected and then ciphered with the produced pseudorandom key-stream. Thus, we achieve a sufficiently robust security level, mostly against known plaintext attacks while preserving a high compression ratio. Several performance analysis including security analysis, speed performances and compression ratio are performed, and demonstrate the efficacy of the proposed approach with respect to existing ones.","PeriodicalId":226712,"journal":{"name":"J. Multim. Process. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126209337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-10-30DOI: 10.18517/IJASEIT.7.5.2765
N. Imran, B. NarendraKumarRao
Iris recognition system is a secure human authentication in biometric technology. Iris recognition system consists of five stages. They are Feature matching, Feature encoding, Iris Normalization, Iris Segmentation and Image acquisition. In Image acquisition, the eye Image is captured from the CASIA database, the Image must have good quality with high resolution to process next steps. In Iris Segmentation, the Iris part is detected by using Hough transform technique and Canny Edge detection technique. Iris from an eye Image segmented. In normalization, the Iris region is converted from the circular region into a rectangular region by using polar transform technique. In feature encoding, the normalized Iris can be encoded in the form of binary bit format by using Gabor filter techniques. In feature matching, the encoded Iris template is compared with database eye Image of Iris template and generated the matching score by using Hamming distance technique and Euclidean distance technique. Based on the matching score, we get the result. This project is developed using Image processing toolbox of Matlab software.
{"title":"Feature Matching in Iris Recognition System using MATLAB","authors":"N. Imran, B. NarendraKumarRao","doi":"10.18517/IJASEIT.7.5.2765","DOIUrl":"https://doi.org/10.18517/IJASEIT.7.5.2765","url":null,"abstract":"Iris recognition system is a secure human authentication in biometric technology. Iris recognition system consists of five stages. They are Feature matching, Feature encoding, Iris Normalization, Iris Segmentation and Image acquisition. In Image acquisition, the eye Image is captured from the CASIA database, the Image must have good quality with high resolution to process next steps. In Iris Segmentation, the Iris part is detected by using Hough transform technique and Canny Edge detection technique. Iris from an eye Image segmented. In normalization, the Iris region is converted from the circular region into a rectangular region by using polar transform technique. In feature encoding, the normalized Iris can be encoded in the form of binary bit format by using Gabor filter techniques. In feature matching, the encoded Iris template is compared with database eye Image of Iris template and generated the matching score by using Hamming distance technique and Euclidean distance technique. Based on the matching score, we get the result. This project is developed using Image processing toolbox of Matlab software.","PeriodicalId":226712,"journal":{"name":"J. Multim. Process. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120988109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-03-08DOI: 10.5815/IJIGSP.2016.03.08
Pranoti P. Mane, N. Bawane
Content-based image retrieval is the process of recovering the images that are based on their primitive features such as texture, color, shape etc. The main challenge in this type of retrieval is the gap between lowlevel primitive features and high-level semantic concepts. This is known as the semantic gap. This paper proposes an interactive approach for optimizing the semantic gap. The primitive features used are HSV histogram, local binary pattern histogram, and color coherence vector histogram. The mapping between primitive features of the image and its semantic concepts is done by involving the user in the feedback loop. Proposed primitive feature extraction method shows improved image retrieval results (Average precision 73.1%) over existing methods. We have proposed an innovative relevance feedback technique in which the concept of prominent features is introduced. On the application of the relevance feedback, only prominent features which are having maximum similarity are utilized. This method reduces the feature length and increases the efficiency. Our own interactive approach for relevance feedback is not only computationally simple and fast but also shows improvement in the retrieval of semantically meaningful relevant images as we go on increasing the iterations.
{"title":"An Interactive Approach for Retrieval of Semantically Significant Images","authors":"Pranoti P. Mane, N. Bawane","doi":"10.5815/IJIGSP.2016.03.08","DOIUrl":"https://doi.org/10.5815/IJIGSP.2016.03.08","url":null,"abstract":"Content-based image retrieval is the process of recovering the images that are based on their primitive features such as texture, color, shape etc. The main challenge in this type of retrieval is the gap between lowlevel primitive features and high-level semantic concepts. This is known as the semantic gap. This paper proposes an interactive approach for optimizing the semantic gap. The primitive features used are HSV histogram, local binary pattern histogram, and color coherence vector histogram. The mapping between primitive features of the image and its semantic concepts is done by involving the user in the feedback loop. Proposed primitive feature extraction method shows improved image retrieval results (Average precision 73.1%) over existing methods. We have proposed an innovative relevance feedback technique in which the concept of prominent features is introduced. On the application of the relevance feedback, only prominent features which are having maximum similarity are utilized. This method reduces the feature length and increases the efficiency. Our own interactive approach for relevance feedback is not only computationally simple and fast but also shows improvement in the retrieval of semantically meaningful relevant images as we go on increasing the iterations.","PeriodicalId":226712,"journal":{"name":"J. Multim. Process. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114134280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In many different application fields the amount and importance of spatio-temporal data (i.e., temporally and/or spatially qualified data) is increasing in last years and users need new solutions for their management. In this paper we propose a spatio-temporal query language, called ST4SQL. The proposed language extends the well-known SQL syntax and the T4SQL temporal query language [4]. The proposed query language deals with different temporal and spatial semantics. These semantics allow one to specify how the system must manage temporal and spatial dimensions for evaluating the queries. Moreover, the query language introduces new constructs for grouping data with respect to temporal and spatial dimensions. Both semantics and grouping constructs take into account and exploit data qualified with granularities.
{"title":"ST4SQL: a spatio-temporal query language dealing with granularities","authors":"G. Pozzani, Combi Carlo","doi":"10.1145/2245276.2245282","DOIUrl":"https://doi.org/10.1145/2245276.2245282","url":null,"abstract":"In many different application fields the amount and importance of spatio-temporal data (i.e., temporally and/or spatially qualified data) is increasing in last years and users need new solutions for their management. In this paper we propose a spatio-temporal query language, called ST4SQL. The proposed language extends the well-known SQL syntax and the T4SQL temporal query language [4]. The proposed query language deals with different temporal and spatial semantics. These semantics allow one to specify how the system must manage temporal and spatial dimensions for evaluating the queries. Moreover, the query language introduces new constructs for grouping data with respect to temporal and spatial dimensions. Both semantics and grouping constructs take into account and exploit data qualified with granularities.","PeriodicalId":226712,"journal":{"name":"J. Multim. Process. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124348449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-12-13DOI: 10.1007/978-3-642-27337-7_38
Mohammad-Hassan Tayarani-Najaran, M. Beheshti, J. Sabet, M. Mobasher, H. Joneid
{"title":"A New Initialization Method and a New Update Operator for Quantum Evolutionary Algorithms in Solving Fractal Image Compression","authors":"Mohammad-Hassan Tayarani-Najaran, M. Beheshti, J. Sabet, M. Mobasher, H. Joneid","doi":"10.1007/978-3-642-27337-7_38","DOIUrl":"https://doi.org/10.1007/978-3-642-27337-7_38","url":null,"abstract":"","PeriodicalId":226712,"journal":{"name":"J. Multim. Process. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121272612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Keim, Leishi Zhang, Milos Krstajic, Svenja Simon
Never before in history data has been generated and collected in such high volumes as it is today. Keeping up to date with the flood of data, using standard tools for data analysis and exploration, is fraught with difficulty. Visual analytics seeks to provide people with better and more effective ways to understand and analyze large datasets, while also enabling them to act upon their findings immediately. The field integrates the analytic capabilities of the computer and the abilities of the human analyst, allowing novel discoveries and empowering individuals to take control of the analytical process. In this paper we present the challenges of visual analytics and exemplify them with a couple of application examples that illustrate the existing potential of current visual analysis techniques but also their limitations.
{"title":"Solving problems with visual analytics: challenges and applications","authors":"D. Keim, Leishi Zhang, Milos Krstajic, Svenja Simon","doi":"10.1145/2024288.2024290","DOIUrl":"https://doi.org/10.1145/2024288.2024290","url":null,"abstract":"Never before in history data has been generated and collected in such high volumes as it is today. Keeping up to date with the flood of data, using standard tools for data analysis and exploration, is fraught with difficulty. Visual analytics seeks to provide people with better and more effective ways to understand and analyze large datasets, while also enabling them to act upon their findings immediately. The field integrates the analytic capabilities of the computer and the abilities of the human analyst, allowing novel discoveries and empowering individuals to take control of the analytical process. In this paper we present the challenges of visual analytics and exemplify them with a couple of application examples that illustrate the existing potential of current visual analysis techniques but also their limitations.","PeriodicalId":226712,"journal":{"name":"J. Multim. Process. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127451187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-07-11DOI: 10.1007/978-3-642-22185-9_23
S. Ghasemi, A. Rahmani
{"title":"Multimedia Streams Retrieval in Distributed Systems Using Learning Automata","authors":"S. Ghasemi, A. Rahmani","doi":"10.1007/978-3-642-22185-9_23","DOIUrl":"https://doi.org/10.1007/978-3-642-22185-9_23","url":null,"abstract":"","PeriodicalId":226712,"journal":{"name":"J. Multim. Process. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114749054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Table has been recognized as a simply and widely used data representation scheme. Each table alone typically contains rich and useful information which is valuable for many applications such as information retrieval, question-answering and etc. While all table formats can simply be parsed by human, this parsing is difficult for computer, prohibiting such applications to be done in an automatic manner. In this paper, we thus propose the comprehensive and novel table interpretation technique, namely tInterpreter. Essentially, it transforms a table into its corresponding horizontal 1-dimensional tables. To achieve this, the underlying work is based on (i) the similarity of two given cells with respect to the data type and the semantic correspondence concerns; (ii) the discovery for the boundary of a primitive table residing in a composite table; (iii) the identification of the attribute-value relationship and the value association of cells; and (iv) the integration of two pieces of similar or dissimilar information. The experimental result showed that the overall effectiveness of tInterpreter was higher than Chen, Tengli and Kim.
{"title":"Mining for attributes and values in tables","authors":"N. Harnsamut, N. Sahavechaphan","doi":"10.1145/1936254.1936264","DOIUrl":"https://doi.org/10.1145/1936254.1936264","url":null,"abstract":"Table has been recognized as a simply and widely used data representation scheme. Each table alone typically contains rich and useful information which is valuable for many applications such as information retrieval, question-answering and etc. While all table formats can simply be parsed by human, this parsing is difficult for computer, prohibiting such applications to be done in an automatic manner. In this paper, we thus propose the comprehensive and novel table interpretation technique, namely tInterpreter. Essentially, it transforms a table into its corresponding horizontal 1-dimensional tables. To achieve this, the underlying work is based on (i) the similarity of two given cells with respect to the data type and the semantic correspondence concerns; (ii) the discovery for the boundary of a primitive table residing in a composite table; (iii) the identification of the attribute-value relationship and the value association of cells; and (iv) the integration of two pieces of similar or dissimilar information. The experimental result showed that the overall effectiveness of tInterpreter was higher than Chen, Tengli and Kim.","PeriodicalId":226712,"journal":{"name":"J. Multim. Process. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127069922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.6025/jmpt/2020/11/3/88-94
Z. Velickovic, Z. Milivojevic, Marko Velickovic
{"title":"A New Watermarking Algorithm for Securing Video Images","authors":"Z. Velickovic, Z. Milivojevic, Marko Velickovic","doi":"10.6025/jmpt/2020/11/3/88-94","DOIUrl":"https://doi.org/10.6025/jmpt/2020/11/3/88-94","url":null,"abstract":"","PeriodicalId":226712,"journal":{"name":"J. Multim. Process. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122136845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.6025/jmpt/2020/11/3/102-109
Ilija Hristoski, Tome Dimovski, V. Manevska
{"title":"NoSQL Graph Databases for e-commerce Applications","authors":"Ilija Hristoski, Tome Dimovski, V. Manevska","doi":"10.6025/jmpt/2020/11/3/102-109","DOIUrl":"https://doi.org/10.6025/jmpt/2020/11/3/102-109","url":null,"abstract":"","PeriodicalId":226712,"journal":{"name":"J. Multim. Process. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116288763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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