Maximum Response Deep Learning Using Markov, Retinal & Primitive Patch Binding With GoogLeNet & VGG-19 for Large Image Retrieval

Smart and productive image retrieval from flexible image datasets is an unavoidable necessity of the current period. Crude picture marks are imperative to mirror the visual ascribes for content-based image retrieval (CBIR). Algorithmically enlightened and recognized visual substance structure image marks to accurately file and recover comparative outcomes. Consequently, highlighted vectors ought to contain adequate image data with color, shape, objects, spatial data viewpoints to recognize image class as a qualifying applicant. This article presents the maximum response of visual features of an image over profound convolutional neural networks in blend with an innovative content-based image retrieval plan to recover phenomenally precise outcomes. For this determination, a serial fusion of GoogLeNet and VGG-19 based generated signatures are formulated with visual features including texture, color and shape. Initially, the maximum response is calculated for texture pattern by using Markov Random Field (MRF) classifier. Thereafter, cascaded samples are passed through a human retinal system like descriptor named Fast Retina Keypoint (FREAK) for corresponding fundamental points through the image. GoogLeNet and VGG-19 are applied to extract deep features of an image; hence color components are obtained using a correlogram. Finally, all the image signatures are combined and passed through the BoW scheme. The proposed method is applied experimentally to challenging datasets, including Caltech-256, ALOT (250), Corel 1000, Cifar-100, and Cifar 10. Remarkable precision,Recall and F-score results obtained.The texture dataset ALOT (250) with the uppermost precision rate 0.99 for a maximum of its categories, whereas Caltech-256 gives 0.66 precision, and Corel 1000 0.99 for VGG-19 and 0.95 for GoogLeNet. Recall, F-score, ARR and ARP rates shows the significant rates in most of the image categories.

Automated summary

This article presents a method of retrieving visual features from flexible image datasets and obtaining accurate results through a combination of deep convolutional neural networks and content-based image retrieval schemes.