Improved Keypoint Matching Method for Near-Duplicate Keyframe Retrieval

Younessian, Ehsan; Rajan, Dinesh; Chng, Eng Siong

doi:10.1109/ism.2009.19

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…However it is observed that PE fails under certain circumstances such as object zooming, change in illumination etc. Pattern coherency measure [18] is proposed in order to handle matching of extreme zooming condition. It should be noted here that our technique handles matching in adaptive manner and is robust to various cases like object zooming, object occlusion etc.…”

Section: Conventional Feature Based Approachesmentioning

confidence: 99%

Adaptive Near Duplicate Image Retrieval Using SURF and CNN Features

Mehta¹,

Bhensdadia²

2019

IJIES

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In this paper, we present an adaptive approach in order to match and retrieve near duplicate images at different scales. Matching only local Features does not necessarily identify visually similar images. Global features are fast at matching but less accurate. Many existing methods either use local features or CNN features for image or video retrieval task. In this paper, we combined the use of SURF local points and CNN features extracted around SURF points in order to match near duplicate image pairs. Image pairs are segmented into blocks and CNN features of the image block containing matched SURF features are extracted and matched. Regions around matched image blocks are grown adaptively and matching is carried out until CNN mismatch is observed. To verify our proposed approach, experiments are carried out on benchmarking California-ND and Holiday dataset. Compared to traditional approaches for image retrieval, our approach not only retrieves relevant images but also provides detail of localized matched patch. For California-ND dataset and Holiday dataset, we achieve remarkable mAP (mean average precision) score up to 0.86 and 0.74 respectively.

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Section: Conventional Feature Based Approachesmentioning

confidence: 99%

Adaptive Near Duplicate Image Retrieval Using SURF and CNN Features

Mehta¹,

Bhensdadia²

2019

IJIES

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“…We use an efficient keypoint matching technique followed by matching pattern analysis in the NDK identification algorithm (Younessian, Rajan, & Chng, 2009) to handle extreme zooming and significant object motion. For each pair of keyframes within the story we calculate a near-duplicate score based on which a weighted adjacency matrix for the graph representation of keyframes is determined.…”

Section: Introductionmentioning

confidence: 99%

Content-Based Keyframe Clustering Using Near Duplicate Keyframe Identification

Rajan

Younessian

2011

International Journal of Multimedia Data Engineering and Management

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In this paper, the authors propose an effective content-based clustering method for keyframes of news video stories using the Near Duplicate Keyframe (NDK) identification concept. Initially, the authors investigate the near-duplicate relationship, as a content-based visual similarity across keyframes, through the Near-Duplicate Keyframe (NDK) identification algorithm presented. The authors assign a near-duplicate score to each pair of keyframes within the story. Using an efficient keypoint matching technique followed by matching pattern analysis, this NDK identification algorithm can handle extreme zooming and significant object motion. In the second step, the weighted adjacency matrix is determined for each story based on assigned near duplicate score. The authors then use the spectral clustering scheme to remove outlier keyframes and partition remainders. Two sets of experiments are carried out to evaluate the NDK identification method and assess the proposed keyframe clustering method performance.

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“…As shown in [117,124,128], the number of matching keypoints is the most discriminative feature in a NDK identification task. In Figure 3.10, we show the scatter diagram of NDKs and non-NDKs for two datasets.…”

Section: Color-based Similaritymentioning

confidence: 99%

A framework for associated news story retrieval

Younessian¹

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Video retrieval-searching and retrieving videos relevant to a given query-is one of the most popular topics in both real life applications and multimedia research. Finding relevant video content is important for producers of television news, documentaries and commercials. Particularly, in news domain, hundreds of news stories in many different languages are being published everyday by the numerous news agencies and media houses. The huge number of published news stories brings enormous challenges in developing techniques for their efficient retrieval. In particular, there is the challenge of identifying two news clips that discuss the same story. Here, the visual information need not be similar enough for simple near-duplicate video detection algorithms to work. Although, visually two news stories might be different, they might be addressing the same main topic. We call such news stories as associated new stories and the main objective in this thesis is to identify such stories. Therefore, it is imperative that we resort to other modalities such as speech and text for robust retrieval of associated news stories. In the visual domain, associated news stories can be seen as duplicate, near-duplicate, partially near-duplicate videos or in more challenging Contents List of Figures viii List of Tables xi LIST OF FIGURES

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Improved Keypoint Matching Method for Near-Duplicate Keyframe Retrieval

Cited by 6 publications

References 13 publications

Adaptive Near Duplicate Image Retrieval Using SURF and CNN Features

Adaptive Near Duplicate Image Retrieval Using SURF and CNN Features

Content-Based Keyframe Clustering Using Near Duplicate Keyframe Identification

A framework for associated news story retrieval

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