Effective Image Retrieval Based on an Experimental Combination of Texture Features and Comparison of Different Histogram Quantizations in the DCT Domain

Fazal Malik and Baharum Baharudin
Computer and Information Sciences Department, Universiti Teknologi PETRONAS, Malaysia

Abstract: The compressed domain is appealing for the image retrieval because of the direct efficient feature extraction; moreover, currently almost all the images are available in a compressed format using the Discrete Cosine Transformation (DCT). In this paper, the quantized histogram statistical texture features are extracted from the DCT blocks using the significant energy of the DC and the first three AC coefficients of the blocks and are used for the retrieval of the similar images. The effectiveness of the image retrieval is analyzed by performing an experimental comparison of the different combinations of the texture features to get an optimum combination and the comparison of the different quantization bins by using the optimum combinations of the features. The proposed approach is tested by using the Corel image database and the experimental results show that the proposed approach has a robust image retrieval using the combinations of the features with the different histogram quantization bins in the frequency domain.

Keywords: Compressed domain, feature extraction, DCT, statistical texture features, quantized histogram.

 

Received July 10, 2012; accepted January 16, 2013

  

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