We propose a content-based image retrieval (CBIR) method based on an efficient combination of a color feature and multiresolution texture features. As a color feature, a HSV autocorrelogram is chosen which is known to measure spatial correlation of colors well. As texture features, BDIP and BVLC moments are chosen which is known to measure local intensity variations well and measure local texture smoothness well, respectively. The texture features are obtained in a wavelet pyramid of the luminance component of a color image. The extracted features are combined for efficient similarity computation by the normalization depending on their dimensions and standard deviation vectors. Experimental results show that the proposed method yielded average 10% better performance in precision vs. recall and average 0.12 in average normalized modified retrieval rank (ANMRR) than the methods using color autocorrelogram, BDIP and BVLC moments, and wavelet moments, respectively.
In this paper, we propose an effective boundary matching based error detection algorithm using causal neighbor blocks in H.263 coded video to improve video quality degraded from channel error. The proposed algorithm first calculates boundary mismatch powers between a current block and one of its causal neighbor blocks. It then decides that a current block should be normal if all the mismatch powers are less than an adaptive threshold, which is adaptively determined using the statistics of the two adjacent blocks. In some expeirments under the environment of 16 bits burst error at bit error rates (BERs) of 10-4~10-3, it is shown that the proposed algorithm yields the improvements of maximum 20% in error detection rate and of maximum 3.5 dB in PSNR of concealed frames, compared with Zeng's error detection algorithm.
An efficient algorithm is proposed for interactive ultrasound image retrieval using magnitude frequency spectrum (MFS). The interactive retrieval is especially intended to be useful for training an intern to diagnose with ultrasound images. In the retrieval process, information on which are relevant to a query image among object images retrieved in the previous iteration is fed back by user interaction. In order to improve discrimination between a query image and each of object images in a database (DB) by using the MFS, which is powerful for ultrasound image retrieval, we incorporate feature vector normalization and root filtering in feature extraction. To effectively integrate the feedback information, we use a feedback scheme based on Rocchio equation, where the feature of a query image is replaced with the weighted average of the feature of a query image and those of object images. Experimental results for real ultrasound images show that while yielding a precision of about 75% at a recall of about 8% in the initial retrieval, the interactive procedure yields a great performance improvement, that is, a precision of about 95% in the third iteration.
In this paper, we first propose new texture features, BDIP (block difference of inverse probabilities) and BVLC (block variation of local correlation coefficients), for content-based image retrieval (CBIR) and then present an image retrieval method based on the combination of BDIP and BVLC moments. BDIP uses the local probabilities in image blocks to measure the variation of brightness well. BVLC uses the variations of local correlation coefficients in images blocks to measure texture smoothness well. Experimental results show that the presented retrieval method yields about 12% better performance than the method using only BDIP or BVLC moments and about 10% better performance than the method using wavelet moments.
We present an efficient algorithm using a region-based texture feature for the extraction of texture regions. The key idea of this algorithm is based on the fact that most of the variations of local correlation coefficients (LCCs) according to different orientations are clearly larger in texture regions than in shade regions. An object image is first segmented into homogeneous regions. The variations of LCCs are next averaged in each segmented region. Based on the averaged variations of LCCs, each region is then classified as a texture or shade region. The threshold for classification is found automatically by an iterative threshold selection technique. In order to evaluate the performance of the proposed algorithm, we use six test images (Lena, Woman, Tank, Jet, Face and Tree) of 256 X 256 8-bit pixels. Experimental results show that the proposed feature suitably extracts the regions that appear visually as texture regions.
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