H.5. Image Processing and Computer Vision
S. Asadi Amiri; Z. Mohammadpoory; M. Nasrolahzadeh
Abstract
Content based image retrieval (CBIR) systems compare a query image with images in a dataset to find similar images to a query image. In this paper a novel and efficient CBIR system is proposed using color and texture features. The color features are represented by color moments and color histograms of ...
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Content based image retrieval (CBIR) systems compare a query image with images in a dataset to find similar images to a query image. In this paper a novel and efficient CBIR system is proposed using color and texture features. The color features are represented by color moments and color histograms of RGB and HSV color spaces and texture features are represented by localized Discrete Cosine Transform (DCT) and localized Gray level co-occurrence matrix and local binary patterns (LBP). The DCT coefficients and Gray level co-occurrence matrix of the blocks are examined for assessing the block details. Also, LBP is used for rotation invariant texture information of the image. After feature extraction, Shannon entropy criterion is used to reduce inefficient features. Finally, an improved version of Canberra distance is employed to compare similarity of feature vectors. Experimental analysis is carried out using precision and recall on Corel-5K and Corel-10K datasets. Results demonstrate that the proposed method can efficiently improve the precision and recall and outperforms the most existing methods.s the most existing methods.
H.5. Image Processing and Computer Vision
V. Patil; T. Sarode
Abstract
Image hashing allows compression, enhancement or other signal processing operations on digital images which are usually acceptable manipulations. Whereas, cryptographic hash functions are very sensitive to even single bit changes in image. Image hashing is a sum of important quality features in quantized ...
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Image hashing allows compression, enhancement or other signal processing operations on digital images which are usually acceptable manipulations. Whereas, cryptographic hash functions are very sensitive to even single bit changes in image. Image hashing is a sum of important quality features in quantized form. In this paper, we proposed a novel image hashing algorithm for authentication which is more robust against various kind of attacks. In proposed approach, a short hash code is obtained by using minimum magnitude Center Symmetric Local Binary Pattern (CSLBP). The desirable discrimination power of image hash is maintained by modified Local Binary Pattern(LBP) based edge weight factor generated from gradient image. The proposed hashing method extracts texture features using the Center Symmetric Local Binary Pattern (CSLBP). The discrimination power of hashing is increased by weight factor during CSLBP histogram construction. The generated histogram is compressed to 1/4 of the original histogram by minimum magnitude CSLBP. The proposed method, has a twofold advantage, first is a small length and second is acceptable discrimination power. Experimental results are demonstrated by hamming distance, TPR, FPR and ROC curves. Therefore the proposed method successfully does a fair classification of content preserving and content changing images.