ICTACT Journal on Image and Video Processing

G. G. Lakshmi Priya ¹, S. Domnic ²

Affiliations
1 School of Information Technology and Engineering, Vellore Institute of Technology, IN
2 Department of Computer Applications, National Institute of Technology-Tiruchirappalli, IN

Abstract

The first and foremost step in the shot boundary detection is the extraction of features from the video sequences. To obtain better performance in shot boundary detection, a new method is proposed in this paper, where texture and local binary information extracted from the hilbert transformed frames are processed and represented as features. The similarity between the frames is constructed as continuity signal. The boundaries between the shots are identified by applying the shot transition identification procedure on the continuity signals. The proposed method is evaluated over the TRECVID 2007 SBD dataset and the performance is compared to the top performers of TRECVID 2007 SBD task.

Keywords

Shot Boundary Detection, Hilbert Transform, Local Binary Information, Texture Feature, Shot Change Transition Identification.

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B. Janet ¹, A. V. Reddy ¹, S. Domnic ¹

Affiliations
1 Department of Computer Applications, National Institute of Technology, Tiruchirappalli, IN

Abstract

In this paper, the incremental codebook generation process, which is a technique for representing a database of images as a single codebook, that captures the content of all the images is proposed. Vector quantization (VQ) is used for creating the codebook of the image. The main problem with VQ is the size of the training sequence that is used to generate the global codebook. This paper explains a method, where the local codebooks are generated for each image. Then, the locally global codebook for the images is computed from the local codebooks by the incremental process. In order to handle large number of images of similar classes, a new method of incrementally creating locally global codebook based on hierarchy of image classes is also explained. It gives an automated representation of the image database compared to the previous work of selecting a random set of representative images, to compute the global codebook. The locally global codebook is used to cluster the images using the encoding distortion. This codebook helps to match images, and retrieve images that are similar to it by comparing the encoding distortion. The precision and recall curve and mean average precision gives better results than the previous works. The classification error is reduced as the distortion is always the smallest for similar images.

Keywords

Vector Quantization, Codebook Generation, Content Based Image Retrieval, Cube Index Model, Image Clustering.

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