Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions

Ashraf, Rehan; Bashir, Khalid; Irtaza, Aun; Mahmood, Muhammad Tariq

doi:10.3390/e17063552

Cited by 77 publications

(46 citation statements)

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“…Authors in (Ashraf et al, 2015), implemented an image representation technique that is based on Bandelet transform. The Bandelet transform restores the geometric boundaries of the main objects detected in an image.…”

Section: Related Workmentioning

confidence: 99%

Combining SURF and MSER along with Color Features for Image Retrieval System Based on Bag of Visual Words

Elnemr

2016

Journal of Computer Science

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Content-Based Image Retrieval (CBIR) has received an extensive attention from researchers due to the rapid growing and widespread of image databases. Despite the massive research efforts consumed for CBIR, the completely satisfactory results have not yet been attained. In this article, we offer a new CBIR technique that relies on extracting Speeded Up Robust Features (SURF) and Maximally Stable Extremal Regions (MSER) feature descriptors as well as the color features; color correlograms and Improved Color Coherence Vector (ICCV). These features are joined and used to build a multidimensional feature vector. Bag-of-Visual-Words (BoVW) technique is utilized to quantize the extracted feature vector. Then, a multiclass Support Vector Machine (SVM) is implemented to classify the query images. The performance of the presented retrieval framework is analyzed and scrutinized by comparing it with three alternative approaches. The first one is based on extracting SURF descriptors while the second one is based on extracting SURF descriptors, color correlograms and ICCV. The third approach, on the other hand, is based on extracting MSER, color correlograms and ICCV. All implemented schemes are tested on two benchmark datasets; Corel-1000 and COIL-100 datasets. The empirical results show that our suggested approach has a superior discriminative classification and retrieval performance with respect to other approaches. The proposed method achieves average precisions of 88 and 93% for the Corel-1000 and COIL-100 datasets, respectively. Moreover, the proposed system has shown a substantial advance in the retrieval precision when compared with other existing systems.

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Section: Related Workmentioning

confidence: 99%

Combining SURF and MSER along with Color Features for Image Retrieval System Based on Bag of Visual Words

Elnemr

2016

Journal of Computer Science

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“…The mean precision and recall values obtained using proposed technique are higher than the recent CBIR techniques [28,[41][42][43][44]. The image retrieval results for the semantic classes of "Mountains" and "Elephants" are presented in Figs.…”

Section: Analysis Of the Evaluation Metrics On The Image Benchmark Ofmentioning

confidence: 87%

“…This shows that increasing the pixel stride decreases the MAP performance and vice versa. In order to present a sustainable performance of the proposed research, the MAP for top-20 retrievals is calculated and compared with recent CBIR techniques [28,[41][42][43][44]. Table 2 and Table 3 are presenting the class-wise comparisons of average precision and recall on the image benchmark of the Corel-1K.…”

Section: Analysis Of the Evaluation Metrics On The Image Benchmark Ofmentioning

confidence: 99%

“…The proposed technique is evaluated using image benchmark of the Corel-1K, and recent CBIR techniques [28,[41][42][43][44] are used for the performance comparison of the proposed technique. The image benchmark of the Corel-1K comprises of 1000 images, which are organized into 10 semantic classes.…”

Section: Analysis Of the Evaluation Metrics On The Image Benchmark Ofmentioning

confidence: 99%

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Scene search based on the adapted triangular regions and soft clustering to improve the effectiveness of the visual-bag-of-words model

Mehmood

Gul

Altaf

et al. 2018

J Image Video Proc.

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The storage size of the image and video repositories are growing day by day due to the extensive use of digital image acquisition devices. The position of an object within an image is obtained by analyzing the content-based properties like shape, texture, and color, while compositional properties present the image layout and include the photographic rule of composition. The high-quality images are captured on the basis of the rule of thirds that divide each image into nine square areas. According to this rule, salient objects of an image are placed on the intersection points or along the imagery lines of the grid to capture the position of the salient objects. To improve image retrieval performance, visual-bag-of-words (VBoW) framework-based image representation is widely used nowadays. According to this framework, the spatial relationship between salient objects of an image is lost due to the formation of a global histogram of the image. This article presents a novel adapted triangular area-based technique, which computes local intensity order pattern (LIOP) features, weighted soft codebooks, and triangular histograms from the four triangular areas of each image. The proposed technique adds the spatial contents from four adapted triangular areas of each image to the inverted index of the VBoW framework, solve overfitting problem of the larger sizes of the codebook, and overwhelmed the problem of the semantic gap. The experimental results and statistical analysis performed on five image collections show an encouraging robustness of the proposed technique that is compared with the recent CBIR techniques.

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“…In order to show the superiority of the proposed technique, the results are also compared with those of Dubey et al [45], Xiao et al [46], Zhou et al [47], Shrivastava et al [48], Kundu et al [49], Zeng et al [50], Walia et al [51], Ashraf et al [52] and ElAlami et al [53]. Table 2 presents the class-wise comparison of the proposed system with comparative systems in terms of average precision values.…”

Section: Precision and Recall Evaluationmentioning

confidence: 99%

An Ensemble Based Evolutionary Approach to the Class Imbalance Problem with Applications in CBIR

et al. 2018

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Abstract: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.

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Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions

Cited by 77 publications

References 50 publications

Combining SURF and MSER along with Color Features for Image Retrieval System Based on Bag of Visual Words

Combining SURF and MSER along with Color Features for Image Retrieval System Based on Bag of Visual Words

Scene search based on the adapted triangular regions and soft clustering to improve the effectiveness of the visual-bag-of-words model

An Ensemble Based Evolutionary Approach to the Class Imbalance Problem with Applications in CBIR

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