758 research outputs found

    Large- Scale Content Based Face Image Retrieval using Attribute Enhanced Sparse Codewords.

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    Content based image retrieval (CBIR) have turn into majority dynamic exploration regions within previous couple of existence. Numerous index strategies be in light of worldwide component circulations. Be that as it may, these worldwide circulations have restricted segregating force since they are not able to catch nearby picture data. Photographs with individuals are the foremost attention of users. Consequently with exponentially increasing pictures, huge size contented base features representation recovery is a facilitating knowledge in favor of various developing applications. The main objective is to apply automatically spotted human characteristics that comprise semantic cue of facade pictures toward increase gratified base facade recovery through creating semantic codeword pro effectual huge size countenance recovery. With leveraging person characteristics into scalable as well as methodical structure, suggest and offer two orthogonal systems named attribute improved meager code and attribute entrenched upturned index toward develop facade recovery. We compare proposed method with other three methods namely LBP, ATTR and SC methods. The results illustrate that the proposed methods can attain qualified enhancement in Mean Average Precision (MAP) associated to the existing methods. DOI: 10.17762/ijritcc2321-8169.15084

    To Improve Content Based Face Retrieval By Creating Semantic Code Words

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    The importance and the complete amount of human face photos make manipulations e.g., search and mining of large-scale human face images a really vital research problem and allow many real world applications. We aim to make use of automatically detected human attributes that contain semantic prompts of the face photos to improve content based face retrieval by constructing semantic code words for efficient large-scale face retrieval. By leveraging human attributes in a scalable and systematic framework we propose two orthogonal methods named attribute-enhanced sparse coding and attribute embedded inverted indexing to perk up the face retrieval in the offline and online stages. We examine the efficiency of different attributes and vital factors necessary for face retrieval. The purpose in this paper is to deal with one of the imperative and challenging problems large-scale content-based face image retrieval. Given a uncertainty face image content-based face image retrieval seeks to find similar face images from a large image database. It is and facilitates equipment for many applications including automatic face annotation crime investigation etc.

    Towards efficient deep neural networks with applications to visual recognition

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    The thesis focuses on the following two topics: designing energy-efficient neural networks and hashing approach to make deep learning more feasible to real applications; deep convolutional neural networks for visual recognition.Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Computer Science, 201

    Adaptable Face Picture Recovery utilizing Quality Upgraded Meager Code words

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    Abstract: Photographs with individuals (e.g., family, companions, Vips, and so on.) are the significant enthusiasm of clients. Consequently, with the exponentially developing photographs, huge scale substance based face picture recovery is an empowering innovation for some rising applications. In this work, we intend to use naturally identified human properties that contain semantic prompts of the face photographs to enhance contentbased face recovery by developing semantic codewords for effective substantial scale face recovery. By leveraging human characteristics in a versatile and deliberate system, we propose two orthogonal systems named characteristic upgraded inadequate coding and attributeembedded modified indexing to enhance the face recovery in the logged off and online stages. We explore the adequacy of distinctive traits and indispensable variables vital for face recovery. Investigating two open datasets, the results demonstrate that the proposed routines can attain up to 43.5% relative change in Guide contrasted with the current systems
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