An Efficient Multi Object Image Retrieval System Using Multiple Features and SVM

Neelima, N.; Reddy, E. Sreenivasa

doi:10.1007/978-3-319-28658-7_22

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…In the following, a brief description of methods based on a combination of low-level features is presented. In [25], the authors used a combination of colour and texture features, while in [26,27] in addition to these two types of features, shape features are also employed. In [28], a colour image is encoded into a sequence of letters, which is used as a feature vector for image retrieval.…”

Section: Related Workmentioning

confidence: 99%

Content-based Image Retrieval Considering Colour Difference Histogram of Image Texture and Edge Orientation

AlyanNezhadi

Qazanfari

Ajam³

et al. 2020

IJE

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Content-based image retrieval is one of the interesting subjects in image processing and machine vision. In image retrieval systems, the query image is compared with images in the database to retrieve images containing similar content. Image comparison is done using features extracted from the query and database images. In this paper, the features are extracted based on the human visual system. Since the human visual system considers the texture and the edge orientation in images for comparison, the colour difference histogram associated with the image's texture and edge orientation is extracted as a feature. In this paper, the features are selected using the Shannon entropy criterion. The proposed method is tested using the Corel-5K and Corel-10K databases. The precision and recall criteria were used to evaluate the proposed system. The experimental results show the ability of the proposed system for more accurate retrieval rather than recently content-based image retrieval systems.

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Section: Related Workmentioning

confidence: 99%

Content-based Image Retrieval Considering Colour Difference Histogram of Image Texture and Edge Orientation

AlyanNezhadi

Qazanfari

Ajam³

et al. 2020

IJE

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“…Computer vision [1] is used to automatically extract, examine and understand useful information of query image and also retrieved images. Normally computer vision algorithms cover a numerous applications such as robots [2] and unmanned vehicle's navigation [3], video encoding on these unmanned vehicles [4], visual slam [5], video tracking [6], image retrieval [7], texture classification [8] , object recognition [9], object categorization [10], image registration [11], face detection [12] , face recognition [13] and also video shot retrieval [14]. According to computer vision, the features can be considered as the visual property of any image.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Real-Time Indian Sign Language Recognition Using Image Fusion

Kurre

Katta

Burla

et al. 2023

Cognitive Science and Technology

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An Efficient Multi Object Image Retrieval System Using Multiple Features and SVM

Cited by 6 publications

References 7 publications

Content-based Image Retrieval Considering Colour Difference Histogram of Image Texture and Edge Orientation

Content-based Image Retrieval Considering Colour Difference Histogram of Image Texture and Edge Orientation

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

Real-Time Indian Sign Language Recognition Using Image Fusion

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