A comparative study of hash based approximate nearest neighbor learning and its application in image retrieval

Arulmozhi, Parvathy; Abirami, S.

doi:10.1007/s10462-017-9591-1

Cited by 5 publications

(1 citation statement)

References 87 publications

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“…This method is works only on the media that measured in content and sematic similarities and it not suitable for media like video, audio etc. Arulmozhi and Abirami [14], consider the importance of the ANN and their general classification; the different categories for learning the hash were analyzed along with their advantageous and disadvantageous. The different bit assignment types were also learned and investigates the various methods to reduces quantization errors along with its pros and cons.…”

Section: Literature Reviewmentioning

confidence: 99%

Joint graph regularization based semantic analysis for cross-media retrieval: a systematic review

Ayyavaraiah¹,

Venkateswarlu²

2018

IJET

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The large number of heterogeneous data are rapidly increasing in the internet and most data consist of audio, video, text and images. The searching of the required data from the large database is difficult and time taking process. The single media retrieval is used to get the needed data from the large dataset and it has the drawback, it can only retrieve the single media only. If the query is given as the text and acquired result are present in text. The users demand the cross-media retrieval for their queries and it is very consistent in providing the result. This helps the users to get more information regarding to their queries. Finding the similarities between the heterogeneous data is very complex. Many research is done on the cross-media retrieval with different methods and provide the different result. The aim is to analysis the different cross-media retrieval with the joint graph regularization (JGR) to understand the various technique. The most of researches are using the parameter of MAP, precision and recall for their research.

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Section: Literature Reviewmentioning

confidence: 99%

Joint graph regularization based semantic analysis for cross-media retrieval: a systematic review

Ayyavaraiah¹,

Venkateswarlu²

2018

IJET

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Time-Quality Tradeoff of MuseHash Query Processing Performance

Pegia,

Lopez,

Moumtzidou

et al. 2024

Lecture Notes in Computer Science

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Nowadays, massive quantities of multimedia data, such as videos, images, text and audio, are generated by various applications on smartphones, drones and other devices. To facilitate efficient retrieval from these multimedia collections, we need (a) effective media representation and (b) efficient indexing and query processing approaches. Recently, the MuseHash approach was proposed, which can effectively represent a variety of modalities, improving on previous hashing-based approaches. However, the interaction of the MuseHash approach with existing indexing and query processing approaches has not been considered. This paper provides a systematic evaluation of a set of state-of-the-art approximate nearest neighbor search algorithms for image retrieval, when applied to the MuseHash approach, providing quantitative comparison results and evaluating the use of High-Performance Computing (HPC) infrastructures. An extensive set of experiments on a benchmark aerial dataset and on a real life-log dataset demonstrates the effectiveness of employing hashing and ANN techniques with HPC, resulting in reduced computational time.

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DSHPoolF: deep supervised hashing based on selective pool feature map for image retrieval

Arulmozhi

Abirami

2020

Vis Comput

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A comparative study of hash based approximate nearest neighbor learning and its application in image retrieval

Cited by 5 publications

References 87 publications

Joint graph regularization based semantic analysis for cross-media retrieval: a systematic review

Joint graph regularization based semantic analysis for cross-media retrieval: a systematic review

Time-Quality Tradeoff of MuseHash Query Processing Performance

DSHPoolF: deep supervised hashing based on selective pool feature map for image retrieval

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