A novel block intensity comparison code for video classification and retrieval

Geetha, M. Kalaiselvi; Palanivel, S.; Ramalingam, V.

doi:10.1016/j.eswa.2008.07.047

Cited by 16 publications

(9 citation statements)

References 14 publications

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“…It was found that GLCMFBHMM outperforms the BICC method for a 22 Dimension (22 D) feature vector and for the optimum number of a 100 Dimension (100 D) feature vector as determined by [56]. The results are shown in table 6.4 also BICC using HMM and in Table 6.5 the classifier used was SVM.…”

Section: Resultsmentioning

confidence: 96%

“…There has been research in animation genre categorization that uses colour [82], but the method developed here does not use colour allowing it to classify older black and white content. It is found that the GLCMHMM has over 85.71% accuracy and outperforms block intensity comparison code (BICC) [56] in this specific classification task using both HMM and SVM.…”

Section: Introductionmentioning

confidence: 95%

“…In [1] a combination of colour, audio, cognitive and structural features was used with textural features. In [56], the block intensity comparison code (BICC) was developed. The BICC characterizes a frame based on each block.…”

Section: Semantic Classificationmentioning

confidence: 99%

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Statistical models of intelligent video-content analysis for cognition

Santarcangelo¹

2022

Preprint

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Video content has a pronounced and varied cognitive impact. This thesis develops several statistical models of video and demonstrates that these models can be used as a means of quantifying how video impacts cognition. This work takes two approaches. For children, systems are developed to classify content based on expert recommendation. The second approach can be applied to adults and works by developing methods to determine extreme ranges of emotions that impact cognition. This thesis first develops decision fusion methods for cognitive classification of children’s video content. It then introduces the novel concept of positive developmental classification of videos for children into videos that are deemed to have a negative or positive impact on cognition from a literature review; a novel system was developed to classify and segment the content accordingly. This study also introduces automatic age-based classification. The work focuses specifically on several high-level audio features as they relate to the cognitive capacity of children. As the impact on cognition of adults is dependent on the intensity of emotions, there is a focus on affective ranking. The main contributions include developing a method to rank and cluster sequences based on their affective content without the granularity problem. Furthermore, this thesis compares the accuracy of several regression methods on the LIRIS database and develops a method to incorporate prior knowledge into the cluster assignments. Then several state-based methods to predict valence and arousal are developed. The first method is the dynamic prediction-hidden Markov model for arousal-time curve estimation in sports videos. This method determines the arousal-time curve by selecting a state sequence that maximizes the joint probability density function between the arousal states and the arousal-time curve. The second method is a novel kernel-based mixture of experts model for linear regression. The latter method outperforms other mixtures of experts models in predicting valence and arousal. As the use of animation as a means of obtaining childrenˆas attention, this thesis introduces a method to automatically categorize different animation genres in a video database made for children by statistically modelling the temporal texture attributes of the video.

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Section: Resultsmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 95%

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Statistical models of intelligent video-content analysis for cognition

Santarcangelo¹

2022

Preprint

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“…Keyframe selection can be performed generally in two different methods namely, sequential and cluster-based [62]. In the sequential technique, video frames are compared sequentially using a threshold [4][5][6]. The following approaches are found in sequential keyframes selection.…”

Section: Introductionmentioning

confidence: 99%

DeepFlower: Archival and Retrieval of Videos

Aradhya,

V K,

Kumar

et al. 2023

Preprint

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This paper presents a model for archival and retrieval of the videos of natural flowers. To design an efficient video retrieval system the stages namely, keyframe selection, feature extraction, feature dimensionality reduction and indexing are essential for fast browsing and accessing of videos. Three different keyframe selection approaches are proposed using clustering algorithms after segmenting flower regions from its background. Deep Convolutional Neural Network is used as a feature extractor. After keyframe selection, a video is represented with a set of keyframes. To reduce the feature dimension of a video, two feature selection methods are utilized. For an efficient archival and fast retrieval of flower videos an indexing method called KD-tree is recommended. For a given query video, similar videos are retrieved both in relative and absolute search modalities. An extensive experimentation conducted on a relatively large flower video dataset. The data set consists of 7788 videos of 30 different species of flowers. The videos are captured with three different devices in different resolutions. The comparative study reveals proposed keyframe selection approaches gives better results. It has also been observed that the videos retrieved in absolute approach with features selected from Binormal separation metric and indexing gives good results.

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“…At the highest level of hierarchy, video database can be categorized into different genres such as cartoon, sports, commercials, news and music and are discussed in [13], [14], and [15]. Video data stream can be classified into various sub categories cartoon, sports, commercial, news and serial are analysis in [2], [3], [7] and [16]. The problems of video genre classification for five classes with a set of visual feature and SVM is used for classification is discussed in [16].…”

Section: Video Classificationmentioning

confidence: 99%

Audio-Video based Classification using SVM and AANN

Subashini¹,

Palanivel²,

Ramalingam³

2012

IJCA

Self Cite

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This paper presents a method to classify audio-video data into one of five classes: advertisement, cartoon, news, movie and songs. Automatic audio-video classification is very useful to audio-video indexing, content based audio-video retrieval. Mel frequency cepstral coefficients are used to characterize the audio data. The color histogram features extracted from the images in the video clips are used as visual features. The experiments on different genres illustrate the results of classification are significant and effective. Experimental results of audio classification and video classification are combined using weighted sum rule for audio-video based classification. The method SVM and AANN classifies the audio-video clips with an accuracy of 95.54%., and 92.94%.

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A novel block intensity comparison code for video classification and retrieval

Cited by 16 publications

References 14 publications

Statistical models of intelligent video-content analysis for cognition

Statistical models of intelligent video-content analysis for cognition

DeepFlower: Archival and Retrieval of Videos

Audio-Video based Classification using SVM and AANN

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