A New Approach to Region Based Image Retrieval using Shape Adaptive Discrete Wavelet Transform

Abstract

In this paper, we present an efficient regionbased image retrieval method, which uses multi-features color, texture and edge descriptors. In contrast to recent image retrieval methods, which use discrete wavelet transform (DWT), we propose using shape adaptive discrete wavelet transform (SA-DWT). The advantage of this method is that the number of coefficients after transformation is identical to the number of pixels in the original region. Since image data is often stored in compressed formats: JPEG 2000, MPEG 4…; constructing image histograms directly in the compressed domain, allows accelerating the retrieval operation time, and reducing computing complexities. Moreover, SA-DWT represents the best way to exploit the coefficients characteristics, and properties such as the correlation. Characterizing image regions without any conversion or modification is first addressed. Using edge descriptor to complement image region characterizing is then introduced. Experimental results show that the proposed method outperforms content based image retrieval methods and recent region based image retrieval methods.

Zero-Shot Deep Learning for Media Mining: Person Spotting and Face Clustering in Video Big Data

“…Belhallouche et al [10] presented an RBIR using shape adaptive discrete wavelet transform (SA-DWT). The features can be extracted using multi-features color, texture, and edge descriptors.…”

Content based Image Retrieval Using Multi Motif Co-Occurrence Matrix

“…The accuracy of a CBIR system depends on the extraction of more important and powerful depiction of the attributes from images. Many meaningful approaches are proposed in the literature for CBIR to extract the physical features such as texture [3], color [4,5], shape or structure [6,7] or a combination of two or more such features. The color indexing [4] and color histogram [5] lack spatial information and that's why they produce false positives.…”

Image Retrieval based Local Motif Patterns Code

“…These region based approaches [5,6] have shown excellent retrieval performance however with high computational time and with too many dimensional features. The methods based on local features are extensively used in various computer vision and image processing applications and achived excellent results [7][8][9][10][11][12][13][14][15] The local features of image are color, shape, texture, edge and etc.… Among these local features the color is the most significant and prominent feature and have profound impact on human perception. The color based methods [16,17] are very popular in CBIR due to their effectiveness and low computational complexity.…”