Convolution, Approximation and Spatial Information Based Object and Color Signatures for Content Based Image Retrieval

Content Based Image Retrieval (CBIR) is a challenging ground to retrieve the images based on feature description and extraction. Salient image features are the potential candidates to search and index the images based on primitive features. This paper presents a novel way to extract and describe the image features based on their object and color signatures. Gaussian of variance is used along with image convolution and the resultant features are approximated using matrix hashing. The high range frequency feature information is then separated to carry out the spatial indexes. High resolution images from versatile image semantic groups are resulted in massive image signatures which are computation effective. Principal component analysis is performed on these feature vectors to obtained reduced statistical information. These grey level information carriers are then combined with RGB channel based spatially arranged features to possess to color and object information. Bags-of-Word (BoW) architecture is applied to these information carriers to index and retrieve the images. Competitive results show that the proposed method outperforms in many categories of benchmarks Caltech-101 and Corel-1000 datasets. The proposed method shows outstanding results in complex and occluded image categories.