Multi-Query Content Based Image Retrieval System using Local Binary Patterns

Joseph, Simily; Balakrishnan, Kalpana

doi:10.5120/2235-2857

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…In order to overcome this issue, Jin et al [14] expand the search space to get more relevant images by combining multiple systems and representations. The recent works in CBIR use multiple input queries such as [2,10,15]. Joseph et al [15] proposed to use multiple input queries and then logical operations for the query images to produce aggregated query.…”

Section: Related Workmentioning

confidence: 99%

“…The recent works in CBIR use multiple input queries such as [2,10,15]. Joseph et al [15] proposed to use multiple input queries and then logical operations for the query images to produce aggregated query. Hsiao et al [10] proposed a multiple queries information retrieval algorithm that combines the Pareto front method (PFM) with efficient manifold ranking (EMR).…”

Section: Related Workmentioning

confidence: 99%

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Improving Cassification Engine in Content based Image Retrieval by Multi-point Queries via Pareto Approach

Nguyen

Ngo

2018

Jour. Inform. Math. Sci.

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Machine learning methods have demonstrated promising performance for Content Based Image Retrieval (CBIR) using Relevance Feedback (RF). However, a very limited number of feedback images can significantly degrade the performance of these techniques. In this work, each image is represented by a vector of multiple distance measures corresponding to multiple features. Each feature is considered a sub-query for RF process. In RF process, we propose to use Pareto method to get Pareto points (also called trade-off points) according to different depths. These points are used as relevant queries for the next RF round. Experimental results show that our proposed approach is very effective to improve the performance of the classification engine.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Improving Cassification Engine in Content based Image Retrieval by Multi-point Queries via Pareto Approach

Nguyen

Ngo

2018

Jour. Inform. Math. Sci.

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“…It is crucial to select the right similarity functions for high retrieval precision and low computational cost. There are various similarity functions that can be used to compare histograms [19,20,23]. We experimented with the similarity functions given in Table 1 and presented a comparison in terms of retrieval precision and running time in Section 5.…”

Section: Proposed Mobile Visual Search Systemmentioning

confidence: 99%

Mobile multi-view object image search

Calisir

Baştan

Ulusoy

et al. 2016

Multimed Tools Appl

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High user interaction capability of mobile devices can help improve the accuracy of mobile visual search systems. At query time, it is possible to capture multiple views of an object from different viewing angles and at different scales with the mobile device camera to obtain richer information about the object compared to a single view and hence return more accurate results. Motivated by this, we propose a new multi-view visual query model on multi-view object image databases for mobile visual search. Multi-view images of objects acquired by the mobile clients are processed and local features are sent to a server, which combines the query image representations with early/late fusion methods and returns the query results. We performed a comprehensive analysis of early and late fusion approaches using various similarity functions, on an existing single view and a new multi-view object image database. The experimental results show that multiview search provides significantly better retrieval accuracy compared to traditional single view search.

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“…The LBP [25][26][27] is a simple method, which integrates different orientations by maintaining a small size of the feature vector. The LBP level varies from 0 to 255 and represents a spatial relationship between nine color levels in a pixel block of size 3 × 3 as shown in Fig.…”

Section: A Primitive Featuresmentioning

confidence: 99%

Image Retrieval by Utilizing Structural Connections within an Image

Mane¹,

Bawane²

2016

IJIGSP

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Content-based image retrieval (CBIR) is broadly applicable for searching digital images from a gigantic database. Images are retrieved by their primitive visual contents such as color, texture, shape, and spatial layout. The approach presented in this paper utilizes structural connections within an image by integrating textured color descriptors and structure descriptors to retrieve semantically significant images. The retrieval results were obtained by applying the HSV histogram, color coherence vector, and local binary pattern histogram to the standard database of Wang et al., which has 1000 images of 10 different semantic categories. Euclidean distance was used to find the similarity between the query image and database images. This method was evaluated against different methods based on edge histogram descriptors, color structure descriptors, color moments, the color histogram, the HSV histogram, Tamura features, edge descriptors, geometrical shape attributes, and statistical properties such as mean, variance, skewness, and kurtosis. Retrieval results obtained using the proposed methods demonstrated a significant improvement in the average precision (73.8% and 73.1%) compared with those obtained using other existing retrieval methods.

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Multi-Query Content Based Image Retrieval System using Local Binary Patterns

Cited by 9 publications

References 24 publications

Improving Cassification Engine in Content based Image Retrieval by Multi-point Queries via Pareto Approach

Improving Cassification Engine in Content based Image Retrieval by Multi-point Queries via Pareto Approach

Mobile multi-view object image search

Image Retrieval by Utilizing Structural Connections within an Image

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