Video Shot Boundary Detection Based on Feature Fusion and Clustering Technique

Abstract

For the problems of low accuracy and high complexity in detection of gradual shot boundary and long shot, a new video shot boundary detection algorithm based on feature fusion and clustering technique (FFCT) is proposed. In the algorithm, the interval frames of video sequence are selected, converted to gray images and scaled by sampling. With the frames, the speed-up robust features (SURF) and fingerprint features are extracted from non-compressed domain and compressed domain, and then the extracted features are fused. Next, K-means method is used to cluster the fused features, and linear discriminant analysis (LDA) is introduced to map the clusters to realize cohesion within classes and looseness among classes. Finally, the correlation of the feature classes between frames is calculated, and the features in each class are selected through density calculation and matched to realize the coarse detection and fine detection of video shot boundary. In the experiment, compared with the latest representative algorithms, it has the highest accuracy for the proposed algorithm. In particular, the detection of gradual shot boundary and long shot are also more accurate. Meanwhile, the average time consumption is also reduced. The experimental results show that the proposed algorithm has high accuracy and time efficiency, especially for gradual shot boundary and long shot detection. INDEX TERMS Shot boundary detection, feature fusion, clustering, mapping.

Semantic Scene Object-Camera Motion Recognition for Scene Transition Detection Using Dense Spatial Frame Segments and Temporal Trajectory Analysis

“…K-mean clustering-based classification technique was proposed in [61]. K-mean Clustering and linear discriminant analysis (LDA) using SURF and DCT fingerprint features were proposed in [40]. Some of the well-trained CNNs were utilized for low-level and high-level feature extraction in [62] and [63].…”

“…The HSI-CNN [12] method achieved a 100% recall rate for both the "Zoom-in" and "Zoom-out" motion, whereas our method achieved lower accuracy. In Table 17, we compare with the feature fusion and K-means clusteringbased (FFCT) method [40] for gradual transition detection on video datasets with low and high dimensional frames. It is observed that our proposed SOCMR method achieved up to 4.45% and 7.29% higher F-1 rates for videos with lower (≤ 352 × 288) and higher (≥ 720 × 1280) frame dimensions, respectively.…”

“…Comparison with FFCT[40] on gradual transition detection on low and high frame dimensionLow frame dimension (≤ 352 × 288)High frame dimension (≥ 720 × 1280)…”

Enriching Shot Boundary Detection through DT-DWT and Deep Learning Models

“…The characteristic merging and segmentation approach algorithm that is discussed in this paper by Feng-Feng Duan et al [11] takes into consideration both the global and indeed the local variables of the movie interval images. It doesn't just recover the characteristics of the condensed region, but additionally recovers the characteristics of the non-compressed zone, which enables it to perform a retrieval and merging of the characteristics that is exhaustive as well as precise.…”

Hybrid feature extraction and optimized deep convolutional neural network based video shot boundary detection

“…Duan et al 16 introduced an SBD system based on feature fusion and clustering technology (FFCT). Sampling process have been selected, converted and scaled the interval frames of the video sequence to detect transitions.…”