Symmetric Image Contents Analysis and Retrieval Using Decimation, Pattern Analysis, Orientation, and Features Fusion

Abstract

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. INDEX TERMSInterest point detection, image extraction, principal component coefficients, image descriptor, sliding window.

Content-based Retrieval of Tiles and Ceramics Images based on Grouping of Images and Minimal Feature Extraction

“…Average Precision Average Time (Sec.) in [1] 61.42% 1.53 in [4] 72.56% 0.67 Proposed 84.57% 0.51 [9] 83.15% in [20] 81.23% in [12] 82.47% in [22] 83.30% in [15] 83.08% in [23] 58.52% in [16] 83.22% in [24] 78.52% in [19] 83.12% in [25] 78.92% in [26] 76.57% in [28] 83.29% in [29] 82.79% Proposed 84.57%…”

“…The performance of the method was investigated on SIMPLIcity, Corel-10k, OT-scene, Oxford flower, and GHIM databases. A feature detector was presented in [29] 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 is applied to interest point features set by using symmetric sampling to cascade matching produced by validating dense distributed receptive fields after estimating perifoveal receptive fields.…”

Retrieval and Characteristic Analysis of Multimedia Tester Based on Bragg Equation

“…The static attributes of videos include video category, format, author, shooting location, name, subject, descriptive content, etc. Select the video content attribute of interest and enter the attribute value to retrieve videos that meet the requirements of the attribute value [12].…”

IRFSC: A high-efficiency hybrid model Image Retrieval Based on Feature Selection Using Continuous Learning Process

“…A growing number of individuals are using various electronic devices and platforms, such as mobile phones, digital cameras, and the internet, as a direct result of technological advancements. As the amount of shared and continuously growing multimedia data increases, researchers will have a tougher time finding or retrieving a relevant image from an archive [1][2][3][4][5]. It is advised to apply Modified color motif co-occurrence matrix for image indexing and retrieval, Image indexing and retrieval in JPEG compressed domain based on vector quantization.…”