An Effective and Fast Hybrid Framework for Color Image Retrieval

Walia, Ekta; Vesal, Sulaiman; Pal, Aman

doi:10.1007/s11220-014-0093-9

Cited by 29 publications

(15 citation statements)

References 23 publications

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“…Color moment has been used at the first level of the retrieval process and then followed by texture and shape descriptors in the second level [15]. An image retrieval system which utilizes only color and shape features by using RGB color model space and Edge directions has also been described [16].…”

Section: B Related State-of-the-art Workmentioning

confidence: 99%

An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System

Shikha¹,

Gitanjali²,

Kumar³

2020

IJIMAI

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The process of searching, indexing and retrieving images from a massive database is a challenging task and the solution to these problems is an efficient image retrieval system. In this paper, a unique hybrid Content-based image retrieval system is proposed where different attributes of an image like texture, color and shape are extracted by using Gray level co-occurrence matrix (GLCM), color moment and various region props procedure respectively. A hybrid feature matrix or vector (HFV) is formed by an integration of feature vectors belonging to three individual visual attributes. This HFV is given as an input to an Extreme learning machine (ELM) classifier which is based on a solitary hidden layer of neurons and also is a type of feed-forward neural system. ELM performs efficient class prediction of the query image based on the pre-trained data. Lastly, to capture the high level human semantic information, Relevance feedback (RF) is utilized to retrain or reformulate the training of ELM. The advantage of the proposed system is that a combination of an ELM-RF framework leads to an evolution of a modified learning and intelligent classification system. To measure the efficiency of the proposed system, various parameters like Precision, Recall and Accuracy are evaluated. Average precision of 93.05%, 81.03%, 75.8% and 90.14% is obtained respectively on Corel-1K, Corel-5K, Corel-10K and GHIM-10 benchmark datasets. The experimental analysis portrays that the implemented technique outmatches many state-of-the-art related approaches depicting varied hybrid CBIR system.

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Section: B Related State-of-the-art Workmentioning

confidence: 99%

An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System

Shikha¹,

Gitanjali²,

Kumar³

2020

IJIMAI

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“…Table 2 shows the results attained by diverse models on the applied image set interms of precision. For comparing purposes, a smart retrieval model [20], NN based model [21], color, texture and shape (CTS) retrieval [22] model, fusion based model [23] and fast hybrid framework (FHF) [24] is employed.…”

Section: Qualitative Analysismentioning

confidence: 99%

An Novel Framework For Content Based Image Retrieval With Quality Assessment System using Optimal Deep Convolution Neural Network

Anandababu*¹,

Kamarasan²

2019

IJITEE

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Content based image retrieval (CBIR) models become popular for retrieving images connected to the query image (QI) from massive dataset. Feature extraction process in CBIR plays a vital role as it affects the system's performance. This paper is focused on the design of deep learning (DL) model for feature extraction based CBIR model. The presented model utilizes a ResNet50 with co-occurrence matrix (RCM) model for CBIR. Here, the ResNet50 model is applied for feature extraction of the QI. Then, the extracted features are placed in the feature repository as a feature vector. The RCM model computes the feature vector for every input image and compares it with the features present in the repository. Then, the images with maximum resemblance will be retrieved from the dataset. In addition, the resemblance between the feature vectors is determined by the use of co-occurrence matrix subtraction process. Besides, structural similarity (SSIM) measure is applied for the validation of the similarity among the images. A comprehensive results analysis takes place by the use of Corel 10K dataset. The experimental outcome indicated the superiority of the RCM model with respect to precision, recall and SSIM.

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“…Walia et al [13] propose also a technique fusion color, texture and shape feature of an image. The first color moments (mean, standard deviation and skewness of colors) calculated from each channel color H-S-V, and then we compare the result vector to with the query one and return the first thirty similaires images.…”

Section: Related Workmentioning

confidence: 99%

Performant Retrieval Image using Rectangular Mask and Combination of Color, Texture and Shape Descriptors

Chifa*¹,

Badri²,

Ruichek³

2019

IJRTE

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The evolution of computer technologies has led to the growth of digital images, which has made the search for similar images in this volume of data a very important research component. Since several works have proposed image search systems entitled CBIR (Content-Based Image Retrieval). This paper presents a new and powerful method for creating CBIR in order to improve the accuracy of search through visual content. The originality of our method lies in its invariance to the rotation of images queries. She consists of applying rectangular masks of different size on the image, and extracting the color descriptor from the visible region on the mask, and then combining the result descriptor to the Uniform Local Binary Pattern (ULBP) texture features and add canny edge features. We compare the query features to the extract ones, using metric distance. We evaluate our techniques using Corel1K and Ukbench dataset. The average precision measured gives good results comparing to the others existing retrieval systems

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An Effective and Fast Hybrid Framework for Color Image Retrieval

Cited by 29 publications

References 23 publications

An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System

An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System

An Novel Framework For Content Based Image Retrieval With Quality Assessment System using Optimal Deep Convolution Neural Network

Performant Retrieval Image using Rectangular Mask and Combination of Color, Texture and Shape Descriptors

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