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  • Open Access


    Deep Neural Network and Pseudo Relevance Feedback Based Query Expansion

    Abhishek Kumar Shukla*, Sujoy Das

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3557-3570, 2022, DOI:10.32604/cmc.2022.022411

    Abstract The neural network has attracted researchers immensely in the last couple of years due to its wide applications in various areas such as Data mining, Natural language processing, Image processing, and Information retrieval etc. Word embedding has been applied by many researchers for Information retrieval tasks. In this paper word embedding-based skip-gram model has been developed for the query expansion task. Vocabulary terms are obtained from the top “k” initially retrieved documents using the Pseudo relevance feedback model and then they are trained using the skip-gram model to find the expansion terms for the user query. The performance of the… More >

  • Open Access


    Enhancing Scalability of Image Retrieval Using Visual Fusion of Feature Descriptors

    S. Balammal@Geetha*, R. Muthukkumar, V. Seenivasagam

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1737-1752, 2022, DOI:10.32604/iasc.2022.018822

    Abstract Content-Based Image Retrieval (CBIR) is an approach of retrieving similar images from a large image database. Recently CBIR poses new challenges in semantic categorization of the images. Different feature extraction technique have been proposed to overcome the semantic breach problems, however these methods suffer from several shortcomings. This paper contributes an image retrieval system to extract the local features based on the fusion of scale-invariant feature transform (SIFT) and KAZE. The strength of local feature descriptor SIFT complements global feature descriptor KAZE. SIFT concentrates on the complete region of an image using high fine points of features and KAZE ponders… More >

  • Open Access


    Hybrid Evolutionary Algorithm Based Relevance Feedback Approach for Image Retrieval

    Awais Mahmood1,*, Muhammad Imran2, Aun Irtaza3, Qammar Abbas4, Habib Dhahri1,5, Esam Mohammed Asem Othman1, Arif Jamal Malik6, Aaqif Afzaal Abbasi6

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 963-979, 2022, DOI:10.32604/cmc.2022.019291

    Abstract Searching images from the large image databases is one of the potential research areas of multimedia research. The most challenging task for nay CBIR system is to capture the high level semantic of user. The researchers of multimedia domain are trying to fix this issue with the help of Relevance Feedback (RF). However existing RF based approaches needs a number of iteration to fulfill user's requirements. This paper proposed a novel methodology to achieve better results in early iteration to reduce the user interaction with the system. In previous research work it is reported that SVM based RF approach generating… More >

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