Abstract
In this paper, a three stage hierarchical image retrieval scheme using a color, texture and shape visual contents (or descriptors) is proposed, since single visual content is not produce an adequate retrieval results effectively. This scheme has reduced the searching space during the image retrieval process at a certain extent due to the hierarchical mode. In initial stage, the shape feature descriptor has been computed by simple fusion of histograms of gradients and invariant moments of segmented image planes. The shape based retrieval process has reduced the search space by discarding the non-relevant images from the universal dataset (or original dataset) effectively and kept the retrieved images into the intermediate dataset. In the second stage, the texture feature descriptors have been computed from the intermediate sub-image dataset by applying the adaptive tetrolet transform on image plane of preprocessed HSV color image. This transform provides the multi-resolution images with finer details by employing the tetrominoes and the proper arrangement of tetrominoes contributes the effective local geometry of image plane. The gray level co-occurrence matrix based texture feature descriptor is obtained by computing second order statistical parameters from each decomposed sub-image. At this stage, the most of the irrelevant images are discarded by retrieving the images from intermediate dataset but still some undesired images are left, those will be handled at the last stage. At this stage, fused color information is captured by applying the color autocorrelogram on both the non-uniform quantized color components of the preprocessed HSV color image. Finally, the color feature descriptor produces the desired retrieval results by discarding the irrelevant images from the texture based sub-image dataset. The proposed scheme has also low computational overhead due to the use of three descriptors at different stages separately. The retrieved results show the better accuracy as compared to the other related visual contents based image retrieval schemes.
Highlights
Content-based Image Retrieval (CBIR) [1], [2] has become proficient research area from the last two decades due toThe associate editor coordinating the review of this manuscript and approving it for publication was Maurizio Tucci.the rapid advancement of multimedia data (i.e image, video and sound) through different sources like social networking sites, high speed Internet, smart phones and image capturing devices
Raghuwanshi et al [21], [22] have suggested tetrolet transform based image retrieval scheme, where they have constructed the texture feature descriptor by computing the means and standard deviations from detailed sub-band images upto 4 levels, but the direct computation of statistical values lacks the spatial information among tetrominoes of the sub-images
Huang et al [32] have developed image retrieval scheme using color, texture and shape visual feature descriptors, where color visual contents have been extracted from an RGB and HSV color images
Summary
Content-based Image Retrieval (CBIR) [1], [2] has become proficient research area from the last two decades due to. A three stage hierarchical image retrieval scheme is proposed using three different kinds of visual feature descriptors i.e., color, texture and shape. It reduces the computational overhead and search space of an image dataset due to the adaptation of three feature descriptors in hierarchical mode. The most desired images have been extracted from the second stage image dataset by discarding the non-relevant images These retrieved images are the final outcomes of the proposed CBIR system.
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