VIDEO DATA MINING PATTERN DISCOVERED USING STATISTICAL MODELING APPROACH

VIDEO DATA MINING PATTERN DISCOVERED USING

STATISTICAL MODELING APPROACH

B.OBULIRAJ

Abstract– The task to discover useful editing patterns from professional video,

such as movie , is one of the main purpose of Video Data Mining. But data mining an multimedia data like a movie is a challenging task due to the complicated contents of a multimedia data particularly the discovered patterns need to be supported by the semantic features, because the features tell the amateur editors how to use the corresponding patterns during the process of editing a new video. Along with this indexing mechanism a histogram-based color descriptors also introduced to reliably capture and represent the color properties of multiple images. Including of this a classification approach is also carried out by the classified associations and by assigning, each of them with a class label, and uses their appearances in the video to construct video indices. Our experimental results demonstrate the performance of the proposed approach.

Index Terms– video indices, video

indexing, histogram

1 INTRODUCTION

Large amounts of image data is being stored in the databases everyday. All the data can only be used efficiently only when the correct data is retrieved successfully.The existing successful systems are text based and are not effective at retrieving the images. For efficient retrieval the image properties are used to retrieve the appropriate images which do not depend on image descriptions.. The proposed system identifies the objects in the images based

on the colour distribution and performs the search. A colour histogram is generated and image segmentation is done to obtain the suitable images.

1.1  Related Work in Video Indexing

The trend of multimedia source digitization has accelerated the combining of many multimedia processing techniques, such as storage, editing, and presentation on computers. Accordingly, techniques are expected for new methods of presenting and creating multimedia content.. However, in the retrieval of video data with motion or shapes, the retrieval method of a user is not necessarily in agreement with the particular data to be retrieved even when the database system provides graphic user interface (GUI) tools. This paper describes a retrieval technique based on gestures for video images. By using gestures, a user can input spatiotemporal information intuitively.

Figure 1 System Architecture

2. THE PROPOSED APPROACH

2.1 Input Video File Processing

In Video File processing applications the

histogram mechanism is proposed. Which utilize the motion histogram for video retrieval, clustering and classification. The videos include different videos (e.g., file downloading, Earth Movement), Folder Copy etc. The duration of each video segment varies from 1 to 5 s and there are a total of 50 frames. We expect various types of motion content in the different videos and therefore, they form a suitable data set in which to test the proposed motion

histogram.

2.2 Indexing Of Frame

For any data retrieval the indexing is treated as an important aspect. As mentioned earlier there are different types of systems that fetch work based on different algorithms and the retrieved

image can be the same query image or a

part of the query image or even objects in the image. Sometimes the query can be “retrieve ten most similar images to the given image” which can be called as the Nearest-neighbour. In the existing systems the performance is proportional to the database size. The efficiency of the system should depend on the number of similar images rather the total number of images in the database.

2. 3 Histogram Search

Histogram search algorithms characterize an image by its color distribution or histogram. A histogram is nothing but a graph that represents all the colors and the level of their occurrence in an image irrespective of the type of the image. Few basic properties about an image can be obtained from using a Histogram. It can be used to set a threshold for screening the images. The shape and the concentration of the colors in the histogram will be the same for similar objects even though they are of different colors..

Figure 2. Color histogram generation

Process

In general any image contains useful and

unwanted information. The system has to differentiate between the both. Consider the below image where the person reading a book is the useful information and the background, people and the market is the unwanted data. The system has to group together the repeated pattern to identify the objects in the image. For example below is given the array for the part of the shirt and this pattern is repeated again

Figure 3 Histogram

Consider the above image where the (small part of the person’s) shirt is enlarged and the respective representation in the form of the array is given. The basic concept behind the histogram generation is simple. Each pixel in the image is scanned and the respective color or intensity value is obtained for the pixel.

iColor = (16 * p1[0]) + p1[1] * 4 + p1[2];

Then a graph is generated with total number of pixels against the pixel intensity. An array variable is chosen to store the different intensities and the counter increases for each repeated intensity counting the total number of occurrences of that particular color or intensity.

iHistoArr[iColor] = iHistoArr[iColor] +1

3 EXPERIMENTAL RESULTS

Different experiments were performed, one for assessing computational performance, and one assessing robustness with respect to validate the methods we have described, we implemented the components of the video frame based retrieval system and tested with a general purpose image database including about 100 videos. The table given below shows the time taken for Splitting Number of frames from the image database.

Table 3.1 Average Search Time

These images are stored in JPEG format

with size 384 _ 256 or 256 _ 384. The system was written in the VB programming language and compiled on Windows platform. In this section, we describe the training concepts and show indexing results.The table given below shows the number of frames spitted from the videos.

Table 3.2 Average Search Time For a

particular frame

Fig. 3.1. The model input frames of

different types of test videos

4. CONCLUSIONS

In this Research, the need for an efficient video based indexing & Video Frame Based retrieval system is identified and the problems with the existing systems are discussed. A new system is proposed for different types of Video Frames and the structure of the Frames and the way they are indexed and stored inside the database are discussed. Different types of images can be processed using the system. The images in the database are compared with the properties of the Video Frame based on color histogram and appropriate results are displayed to the user. The architecture is designed by studying different existing systems and current research areas. Research in the fields of image processing, segmentation, edge detection, pattern recognition and more is performed to design the system

.

5. REFERENCES

[1] Nevenka Dimitrova, Hong-Jiang Zhang, Behzad Shahraray, Ibrahim Sezan, Thomas Huang, and Avideh Zakhor, “Applications of video-content analysis and retrieval,” IEEE Multimedia Magazine, vol. 9, no. 3, pp. 42 – 55, July 2002.

[2] Asif 2005, CoderSource, 18 June 2009,

[3] Tang L 2007, University of Surrey, 18 June 2009,

[4] R. Benmokhtar and B. Huet,“Neural

network combining classifier based on Dempster-Shafer theory for semantic indexing in video content,” International MultiMedia Modeling Conference, vol.

4351, pp. 196–205, Singapore, 2007.

[5] D.K. Park, Y.S. Jeon and C.S. Won,

“Efficient use of local edge histogram descriptor,”ACM Workshops on Multimedia, pp. 51–54, USA, 2000.

[6] M. Rautiainen and T. Seppanen,“Comparison of visual features and fusion techniques in automatic detection of concepts from news video,” IEEE International Conference on Multimedia & Expo, The Netherlands, 2005.

[7] E. Allwein, R. Schapire, and Y. Singer, “Reducing multiclass to binary : A unifying approach for margin classifiers.” Journal of Machine Learning Research, vol. 1, pp.  113–141, 2000.

[8] Steyvers, M., Smyth, P., Rosen-Zvi, M., & Griffiths, T.(2004). Probabilistic Author- Topic Models for Information Discovery. The Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Seattle, Washington.

[9] Tardini, G. Grana C., Marchi, R., Cucchiara, R., (2005). Shot Detection and Motion Analysis for Automatic MPEG-7 Annotation of Sports Videos. In 13^th International Conference on Image Analysis and Processing.

[10] Witten, I. and Frank, E. (2005) "Data Mining: Practical machine learning tools and techniques", 2nd Edition, Morgan Kaufmann, San Francisco, 2005

[11] Worring, M., Snoek, C.. (2006).

Semantic Indexing and Retrieval of Video. Tutorial at ACM Multimedia.