Introduction

Abstract. In this paper, we present VSCAN, a novel approach for generating static video summaries. This approach is based on a modified DBSCAN clustering algorithm to summarize the video content utilizing both color and texture features of the video frames. The paper also introduces an enhanced evaluation method that depends on color and texture features. Video Summaries generated by VSCAN are compared with summaries generated by other approaches found in the literature and those created by users. Experimental results indicate that the video summaries generated by VSCAN have a higher quality than those generated by other approaches.

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“…The F-measure consolidates both Precision and Recall values into one value using the harmonic mean [4], and it is defined as:…”

Section: Experimental Evaluationmentioning

confidence: 99%

VSCAN: An Enhanced Video Summarization Using Density-Based Spatial Clustering

Mahmoud

Ismail

Ghanem

2013

Image Analysis and Processing – ICIAP 2013

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“…With huge data available, getting the relevant data that queried by users is challenging, whereas more and more people have high expectation retrieving the desired information precisely and relevantly. Those facts make the multimedia retrieval system become important topic research in this digital era [1].…”

Section: Introductionmentioning

confidence: 99%

Using Latent Semantic Index for Content-Based Image Retrieval

2015

IJSR

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Abstract:In this paper, the latent semantic indexing (LSI) based method was used to the various image feature extracted matrix in order to perform content-based image retrieval. The feature extraction techniques include color histogram, color auto-gram, color moment, gray-scale, and wavelet moment. The implementation of LSI here is to achieve an improved image retrieval performance, because it reduces the size of input image matrix by determining parameter of k. The objective of this paper is to know the performance of LSI-based CBIR using precision and recall parameters for each feature extractions.

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“…In the last decade, the research concerning similarity search in multimedia databases underwent a long way towards effective and efficient retrieval [2,6,4]. The two basic similarity query types, the range query and the k nearest neighbors (kNN) query, have been adopted as a simple yet powerful model of content-based query-by-example similarity search paradigm.…”

Section: Introductionmentioning

confidence: 99%

Distinct nearest neighbors queries for similarity search in very large multimedia databases

Skopal

Dohnal

Batko

et al. 2009

Proceedings of the Eleventh International Workshop on Web Information and Data Management

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As the volume of multimedia data available on internet is tremendously increasing, the content-based similarity search becomes a popular approach to multimedia retrieval. The most popular retrieval concept is the k nearest neighbor (kNN) search. For a long time, the kNN queries provided an effective retrieval in multimedia databases. However, as today's multimedia databases available on the web grow to massive volumes, the classic kNN query quickly loses its descriptive power. In this paper, we introduce a new similarity query type, the k distinct nearest neighbors (kDNN), which aims to generalize the classic kNN query to be more robust with respect to the database size. In addition to retrieving just objects similar to the query example, the kDNN further ensures the objects within the result have to be distinct enough, i.e. excluding near duplicates.

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Introduction

Cited by 13 publications

References 11 publications

VSCAN: An Enhanced Video Summarization Using Density-Based Spatial Clustering

VSCAN: An Enhanced Video Summarization Using Density-Based Spatial Clustering

Using Latent Semantic Index for Content-Based Image Retrieval

Distinct nearest neighbors queries for similarity search in very large multimedia databases

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