Research Of Scene Classification Algorithm Based On Hypercomplex DCT Domain

Along with the rapid development and wide application of the modern network and computer technology, vast amounts of digital image were published and shared everyday. The explosion of image resources provides great convenience to people's life, at the same time, it also bring challenge to people to find the useful information from voluminous image data quickly. How to analysis, organization and management image data effectively, to realize the scene classification based on image content has become a hot topic in the field of image processing. The difficulty of the research is how to make computer able to understand the semantic information of the images from the perspective of human cognitive.In order to improve speed and accuracy of the scene classification algorithm, combine texture features of DCT transform and color space parallelism feature of hypercomplex, this paper studied DCT transform first, proposed three kinds of classification algorithms in the DCT transform domain, on the basis of these algorithms, by introducing hypercomplex proposed a scene classification algorithm in the hypercomplex DCT transform domain finally. The main research contents of this thesis are as follows?:(1) Existing image classification algorithms are all implemented in the uncompressed domain and the classification speed is slowly. In order to solve this problem, this thesis proposed a fast scene classification algorithm based on the discrete cosine transform(DCT) Domain. The coefficients after DCT transform in frequency domain have “energy concentration” and “multi-scale multi-resolution” characteristics, these characteristics can well reflect the image texture and easy to quantitative analysis. If choose appropriate size of transform block, DCT transformation also has good quickness. Extract image features in DCT domain according to these characteristics, then with the features do classification experiment. Experimental results show that the proposed algorithm improved the classification speed greatly.(2) In order to improve the classification accuracy further, according to the idea of the spectral residual, this thesis proposed another scene classification algorithm based on residual vectors. Experimental results show that the proposed algorithm has higher classification accuracy and speed, and also has good robustness.(3) The residual vectors algorithm achieved the high classification accuracy and speed at the same time, but the algorithm belongs to semi-supervised classification. Aiming at this problem, this thesis proposed a bag-of-words classification algorithm in DCT transform domain. Defined DCT feature vector in fast scene classification algorithm as the feature descriptor, modeling words vocabulary by the algorithm of bag of words model. The algorithm not only improved the classification accuracy but also improved the classification speed.(4) The three kinds of algorithms mentioned above and the existing classic scene classification algorithms are all based on the gray image or treat the color channel respectively. In this thesis, through introducing the hypercomplex, using color information constitute hypercomplex, then do hypercomplex DCT transform, extracting coefficients feature in frequency domain for scene classification, make full use of the correlation of low-level features(color and texture feature) of the image. The experimental results show that the algorithm improved the classification accuracy further.