A Novel Technique Based on Visual Words Fusion Analysis of Sparse Features for Effective Content-Based Image Retrieval

Yousuf, Muhammad; Mehmood, Zahid; Habib, Hafiz Adnan; Mahmood, Toqeer; Saba, Tanzila; Rehman, Amjad; Rashid, Muhammad

doi:10.1155/2018/2134395

Cited by 58 publications

(42 citation statements)

References 48 publications

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“…Early image retrieval algorithms used the SIFT feature, which focused on generating image representations using local feature descriptors and aggregation strategies to describe the features of an image, for example, visual bag-of-words (BoW) and variants of various word-package models, such as VLAD, Fisher vector, and triangulation embedding. Furthermore, Yousuf et al improved the performance of CBIR on the basis of visual words fusion of the SIFT and local intensity order pattern (LIOP) descriptors [20].…”

Section: Related Workmentioning

confidence: 99%

Multilayer Convolutional Feature Aggregation Algorithm for Image Retrieval

Dong

Liu

2019

Mathematical Problems in Engineering

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In image retrieval tasks, the single-layer convolutional feature has insufficient image semantic representation ability. A new image description algorithm ML-RCroW based on multilayer multiregion cross-weighted aggregational deep convolutional features is proposed. First, the ML-RCroW algorithm inputs an image into the VGG16 (a deep convolutional neural network developed by researchers at Visual Geometry Group and Google DeepMind) network model in which the fully connected layer is discarded. The visual feature information in the convolutional neural network (CNN) is extracted, and the target response weight map is generated by combining with the spatial weighting algorithm of the target fuzzy marker. Then, visual features in the CNN are divided into multiple regions, and the pixels of each region are weighted by regional spatial weight, regional channel weight, and regional weight. The image global vector is generated by aggregating and encoding every region in the weighted feature map. Finally, features of each layer of the VGG16 network model are extracted and then aggregated and dimensionally reduced to obtain the final feature vector of the image. The experiments are carried out on the Oxford5k and Paris6k datasets provided by Oxford VGG. The experimental results show that the average accuracy of image retrieval based on the image feature description algorithm ML-RCroW is better than that achieved by the other commonly used algorithms such as SPoC, R-MAC, and CroW.

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Section: Related Workmentioning

confidence: 99%

Multilayer Convolutional Feature Aggregation Algorithm for Image Retrieval

Dong

Liu

2019

Mathematical Problems in Engineering

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“…Finally, comparing with articles [30,[58][59][60] that use other image data bases (Corel A, Corel B, and Caltech) which include some natural scenarios: costs, flowers, and mountains. It can be seen in Table 4 that this proposal is superior.…”

Section: Comparison To Previous Classificationmentioning

confidence: 99%

“…Research and development in CBIR technique enabled to make its place in the market in the form of co-marketing products such as QBIC-IBM (http://www.qbic.almaden.ibm.com/), VisualSEEL (http:// www.ee.columbia.edu/ln/dvmm/researchProjects/Multimedia-Indexing/VisualSEEk/VisualSEEk.htm), and MARS (https:// www.ideals.illinois.edu/handle/2142/25947). The increasing attention given and search for efficient methods have made it an active research area [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Topology: A Theory of a Pseudometric-Based Clustering Model and Its Application in Content-Based Image Retrieval

Osuna-Galán

Pérez-Pimentel

Avilés‐Cruz

et al. 2019

Mathematical Problems in Engineering

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The clustering problem has been extensively studied over the last 50 years; however, it still has the attention of researchers. This paper presents a topological basis of a pseudometric-based clustering model which takes into account the local and global topological properties of the data to be clustered, as per the definition of homogeneity measurement. The proposed approach takes into account the homogeneity effect produced when a new particle is added to a group. The additional element can be accumulated in the group if its local homogeneity is not altered and, therefore, it is not necessary to carry out tests in another group. A new group needs to be generated if the threshold of the local homogeneity of the group exceeds. Theoretical results, their implementation, and their application to the problem of Content Based Image Retrieval (CBIR) are presented. The tests were performed using three image databases widely used in the literature, which are “Vogel and Shiele,” “Oliva and Torralba,” and “L. Fei- Fei, R. Fergus and P. Perona.” The results are presented and compared with the most competitive methods available in the literature.

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“…Sood and Mishra [32] presented the system that takes images of sign language as input and displays speech as output. The features used in vision-based approaches for speech processing are also used in different object recognition based applications [33][34][35][36][37][38][39].…”

Section: Vision-based Technology Approachmentioning

confidence: 99%

A Novel Technique for Speech Recognition and Visualization Based Mobile Application to Support Two-Way Communication between Deaf-Mute and Normal Peoples

Yousaf

Mehmood

Saba

et al. 2018

Wireless Communications and Mobile Computing

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Mobile technology is very fast growing and incredible, yet there are not much technology development and improvement for Deafmute peoples. Existing mobile applications use sign language as the only option for communication with them. Before our article, no such application (app) that uses the disrupted speech of Deaf-mutes for the purpose of social connectivity exists in the mobile market. The proposed application, named as vocalizer to mute (V2M), uses automatic speech recognition (ASR) methodology to recognize the speech of Deaf-mute and convert it into a recognizable form of speech for a normal person. In this work mel frequency cepstral coefficients (MFCC) based features are extracted for each training and testing sample of Deaf-mute speech. The hidden Markov model toolkit (HTK) is used for the process of speech recognition. The application is also integrated with a 3D avatar for providing visualization support. The avatar is responsible for performing the sign language on behalf of a person with no awareness of Deaf-mute culture. The prototype application was piloted in social welfare institute for Deaf-mute children. Participants were 15 children aged between 7 and 13 years. The experimental results show the accuracy of the proposed application as 97.9%. The quantitative and qualitative analysis of results also revealed that face-to-face socialization of Deaf-mute is improved by the intervention of mobile technology. The participants also suggested that the proposed mobile application can act as a voice for them and they can socialize with friends and family by using this app.

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A Novel Technique Based on Visual Words Fusion Analysis of Sparse Features for Effective Content-Based Image Retrieval

Cited by 58 publications

References 48 publications

Multilayer Convolutional Feature Aggregation Algorithm for Image Retrieval

Multilayer Convolutional Feature Aggregation Algorithm for Image Retrieval

Topology: A Theory of a Pseudometric-Based Clustering Model and Its Application in Content-Based Image Retrieval

A Novel Technique for Speech Recognition and Visualization Based Mobile Application to Support Two-Way Communication between Deaf-Mute and Normal Peoples

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