Object detection and classification: a joint selection and fusion strategy of deep convolutional neural network and SIFT point features

Rashid, Muhammad; Khan, Muhammad Attique; Sharif, Muhammad; Raza, Mudassar; Sarfraz, M. Saquib; Afza, Farhat

doi:10.1007/s11042-018-7031-0

Cited by 87 publications

(40 citation statements)

References 48 publications

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“…In image processing, features are extracted to represent the information of an image and the objects. Features extraction is an important step in the area of pattern recognition (PR) and machine learning (Rashid et al, ). In medical field, these extracted features are fed to classifier for the classification of selected diseases (Khan et al).…”

Section: Proposed Methodologymentioning

confidence: 99%

“…An appropriate features selection method selects robust features and significantly enhances performance of the system. A lot of techniques are newly introduced to select best subgroup of features from an originally obtained vector including grass hopper, genetic algorithm (GA), entropy selection, and Particle Swarm Optimization (Khan, Lali, et al, ; Rashid et al, ). These techniques performed satisfactorily in both time computation and accuracy performance.…”

Section: Introductionmentioning

confidence: 99%

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Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection

Majid

Khan

Yasmin

et al. 2020

Microscopy Res & Technique

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126

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Automated detection and classification of gastric infections (i.e., ulcer, polyp, esophagitis, and bleeding) through wireless capsule endoscopy (WCE) is still a key challenge. Doctors can identify these endoscopic diseases by using the computer-aided diagnostic (CAD) systems. In this article, a new fully automated system is proposed for the recognition of gastric infections through multi-type features extraction, fusion, and robust features selection. Five key steps are performed-database creation, handcrafted and convolutional neural network (CNN) deep features extraction, a fusion of extracted features, selection of best features using a genetic algorithm (GA), and recognition. In the features extraction step, discrete cosine transform, discrete wavelet transform strong color feature, and VGG16-based CNN features are extracted. Later, these features are fused by simple array concatenation and GA is performed through which best features are selected based on K-Nearest Neighbor fitness function. In the last, best selected features are provided to Ensemble classifier for recognition of gastric diseases. A database is prepared using four datasets-Kvasir, CVC-ClinicDB, Private, and ETIS-LaribPolypDB with four types of gastric infections such as ulcer, polyp, esophagitis, and bleeding. Using this database, proposed technique performs better as compared to existing methods and achieves an accuracy of 96.5%. K E Y W O R D S

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection

Majid

Khan

Yasmin

et al. 2020

Microscopy Res & Technique

Self Cite

126

View full text Add to dashboard Cite

show abstract

“…Due to the challenges and need, text classification has high demand. Recently, deep learning shows a huge advancement in many machine learning research areas such as text/signature [6], object classification [21,47], visual surveillance [26,51], biometrics [3], medical imaging [27,29,42,48,52], and many more [4,25,28,[30][31][32]41]. In this research, a deep learning model is proposed for detecting similarity between short sentences such as question pairs.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning based Semantic Similarity Detection using Text Data

Mansoor¹,

Rehman²,

Shaheen³

et al. 2020

ITC

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Similarity detection in the text is the main task for a number of Natural Language Processing (NLP) applications. As textual data is comparatively large in quantity and huge in volume than the numeric data, therefore measuring textual similarity is one of the important problems. Most of the similarity detection algorithms are based upon word to word matching, sentence/paragraph matching, and matching of the whole document. In this research, a novel approach is proposed using deep learning models, combining Long Short Term Memory network (LSTM) with Convolutional Neural Network (CNN) for measuring semantics similarity between two questions. The proposed model takes sentence pairs as input to measure the similarity between them. The model is tested on publicly available Quora’s dataset. The model in comparison to the existing techniques gave 87.50 % accuracy which is better than the previous approaches.

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“…For example, there were more and more image mosaics combined traditional SIFT and SURF algorithms with deep learning technology. Some researchers combined the feature points extracted by convolutional neural networks such as VGG and AlexNet with the feature points extracted by SIFT, which proved that this method had great research value (Rashid et al,2019;Shao et al,2019).…”

Section: Introductionmentioning

confidence: 99%

A New Method of Fast Registration of Unmanned Aerial Vehicle Remote Sensing Images Based-on an Improved Surf Algorithm

Lei

Cheng

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Remote sensing system fitted on UAV (Unmanned Aerial Vehicle) can obtain clear images and high-resolution aerial photographs. It has advantages of flexibility, convenience and ability to work full-time. However, there are some problems of UAV image such as small coverage area, large number, irregular overlap, etc. How to obtain a large regional map quickly becomes a major obstacle to UAV remote sensing application. In this paper, a new method of fast registration of UAV remote sensing images was proposed to meet the needs of practical application. This paper used Progressive Sample Consensus (PROSAC) algorithm to improve the matching accuracy by removed a large number of mismatching point pairs of remote sensing image registration based-on SURF (Speed Up Robust Feature) algorithm, and GPU (Graphic Processing Unit) was also used to accelerate the speed of improved SURF algorithm. Finally, geometric verification was used to achieve mosaic accuracy in survey area. The number of feature points obtained by using improved SURF based-on PROSAC algorithm was only 9.5% than that of SURF algorithm. Moreover, the accuracy rate of improved method was about 99.7%, while the accuracy rate of improved SURF algorithm was increased by 8% than SURF algorithm. Moreover, the improved running time of SURFGPU algorithm for UAV remote sensing image registration was a speed of around 16 times than SURF algorithm, and the image matching time had reached millisecond level. Thus, improved SURF algorithm had better matching accuracy and executing speed to meet the requirements of real-time and robustness in UAV remote sensing image registration.

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Object detection and classification: a joint selection and fusion strategy of deep convolutional neural network and SIFT point features

Cited by 87 publications

References 48 publications

Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection

Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection

Deep Learning based Semantic Similarity Detection using Text Data

A New Method of Fast Registration of Unmanned Aerial Vehicle Remote Sensing Images Based-on an Improved Surf Algorithm

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