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.

Intelligent and efficient image retrieval from versatile image datasets is an inevitable requirement of the current era. Primitive image signatures are vital to reflect the visual attributes for content based image retrieval (CBIR). Algorithmically descriptive and well identified visual contents form the image signatures to correctly index and retrieve similar results. Hence feature vectors should contain ample image information with color, shape, objects, spatial information perspectives to distinguish image category as a qualifying candidate. This contribution presents a novel features detector by locating the interest points by applying non-maximum suppression to productive sum of derivative of pixels computed from differential of corner scores. The interest points are described by applying scale space interpolation to scale space division produced from Hessian blob detector resulted after Gaussian smoothing. The computed shape and object information is fused with color features extracted from the spatially arranged L2 normalized coefficients. High variance coefficients are selected for object based feature vectors to reduce the massive data which in fuse form transformed to bag-of-words (BoW) for efficient retrieval and ranking. To check the competitiveness of the presented approach it is experimented on nine well-known image datasets Caltech-101, ImageNet, Corel-10000, 17-Flowers, Columbia object image library (COIL), Corel-1000, Caltech-256, tropical fruits and Amsterdam library of textures (ALOT) belong to shape, color, texture, and spatial & complex objects categories. Extensive experimentation is conducted for seven benchmark descriptors including maximally stable extremal region (MSER), speeded up robust features (SURF), difference of Gaussian (DoG), red green blue local binary pattern (RGBLBP), histogram of oriented gradients (HOG), scale invariant feature transform (SIFT), and local binary pattern (LBP). Remarkable outcomes reported that the presented technique has significant precision rates, recall rates, average retrieval precision & recall, mean average precision & recall rates for many image semantic groups of the challenging datasets. Results comparison is presented with research techniques and reported improved results.

In order to lower the dependence on textual annotations for image searches, the content based image retrieval (CBIR) has become a popular topic in computer vision. A wide range of CBIR applications consider classification techniques, such as artificial neural networks (ANN), support vector machines (SVM), etc. to understand the query image content to retrieve relevant output. However, in multi-class search environments, the retrieval results are far from optimal due to overlapping semantics amongst subjects of various classes. The classification through multiple classifiers generate better results, but as the number of negative examples increases due to highly correlated semantic classes, classification bias occurs towards the negative class, hence, the combination of the classifiers become even more unstable particularly in one-against-all classification scenarios. In order to resolve this issue, a genetic algorithm (GA) based classifier comity learning (GCCL) method is presented in this paper to generate stable classifiers by combining ANN with SVMs through asymmetric and symmetric bagging. The proposed approach resolves the classification disagreement amongst different classifiers and also resolves the class imbalance problem in CBIR. Once the stable classifiers are generated, the query image is presented to the trained model to understand the underlying semantic content of the query image for association with the precise semantic class. Afterwards, the feature similarity is computed within the obtained class to generate the semantic response of the system. The experiments reveal that the proposed method outperforms various state-of-the-art methods and significantly improves the image retrieval performance.

Image retrieval procedures locate interest points and consider the features as the visual property of an image in computer vision. These primitive features define visual attributes as local or global features for content based image retrieval. The visual attributes of an image, include spatial information, shape, texture, object and color, describe the image category. The present research contributes feature detector by performing non-max suppression after detecting edges and corners based on corner score and pixel derivation-based shapes on intensity-based interest points. Thereafter, interest point description applied on interest point features set by applying symmetric sampling to cascade matching produced by validating dense distributed receptive fields after estimating perifoveal receptive fields. Spatial color-based features vector are fused with retinal and color-based feature vector extracted after applying L2 normalization on spatially arranged color image. Dimensions are reduced by applying PCA on massive feature vectors produced after symmetric sampling and transmitted to bag-of-word in fused form for indexing and retrieval of images. Extensive experiments are performed on well-known benchmarks corel-1000, core-10000, caltech-101, image net, alot, coil, ftvl, 102-flowers and 17-flowers. In order to measure the competitiveness, we designed a comparison of proposed method with seven descriptors and detectors RGBLBP, LBP, surf, sift, DoG, HoG and MSER. The proposed method reports remarkable AP, AR, ARP, ARR, P&R, mAP and mAR rates in many categories of image datasets.