Block-Based Methods for Image Retrieval Using Local Binary Patterns

Takala, Valtteri; Ahonen, Timo; Pietikäinen, Matti

doi:10.1007/11499145_89

Cited by 162 publications

(70 citation statements)

References 18 publications

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“…Different approaches for image representation have been proposed including by Feng et al (2004), Takala et al (2005), Tieu and Viola (2004). In the information retrieval community there is wide agreement that a bag-of-words representation is a very useful representation for handling text documents in a wide range of applications.…”

Section: Image Representationmentioning

confidence: 99%

“…For edge histograms, we rely on uniform Local Binary Patterns (uLBPs) proposed by Ojala et al (2002). These texture descriptors have shown to be effective on various tasks in the computer vision literature (Ojala et al, 2002, Takala et al, 2005, certainly due to their robustness with respect to changes in illumination and other photometric transformations (Ojala et al, 2002). Local Binary Patterns estimate a texture histogram of a block by considering differences in intensity at circular neighborhoods centered on each pixel.…”

Section: Image Representationmentioning

confidence: 99%

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Large Scale Online Learning of Image Similarity through Ranking

Chechik

Sharma

Shalit

et al. 2009

Lecture Notes in Computer Science

422

624

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Learning a measure of similarity between pairs of objects is an important generic problem in machine learning. It is particularly useful in large scale applications like searching for an image that is similar to a given image or finding videos that are relevant to a given video. In these tasks, users look for objects that are not only visually similar but also semantically related to a given object. Unfortunately, the approaches that exist today for learning such semantic similarity do not scale to large datasets. This is both because typically their CPU and storage requirements grow quadratically with the sample size, and because many methods impose complex positivity constraints on the space of learned similarity functions.The current paper presents OASIS, an Online Algorithm for Scalable Image Similarity learning that learns a bilinear similarity measure over sparse representations. OASIS is an online dual approach using the passive-aggressive family of learning algorithms with a large margin criterion and an efficient hinge loss cost. Our experiments show that OASIS is both fast and accurate at a wide range of scales: for a dataset with thousands of images, it achieves better results than existing state-of-the-art methods, while being an order of magnitude faster. For large, web scale, datasets, OASIS can be trained on more than two million images from 150K text queries within 3 days on a single CPU. On this large scale dataset, human evaluations showed that 35% of the ten nearest neighbors of a given test image, as found by OASIS, were semantically relevant to that image. This suggests that query independent similarity could be accurately learned even for large scale datasets that could not be handled before.

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Section: Image Representationmentioning

confidence: 99%

Section: Image Representationmentioning

confidence: 99%

Large Scale Online Learning of Image Similarity through Ranking

Chechik

Sharma

Shalit

et al. 2009

Lecture Notes in Computer Science

422

624

View full text Add to dashboard Cite

show abstract

“…That is LBP takes a value 1 for any difference (min to max) of values. Therefore, if there is lack of ones, the basic LBP will take only 0 value, which means that the real number of possible textures are 2 8 , i.e., 256. To overcome the above, the fuzzy membership function is introduced by the present study on LBP.…”

Section: The Proposed Methods Of Run Length Matrix On Fuzzy Local Binamentioning

confidence: 99%

“…Image texture analysis is an important fundamental problem in computer vision. During the past few years, several authors have developed theoretically and computationally simple, but very efficient nonparametric methodology for texture analysis based on LBP [2,3,4,5,6,7,8,9]. The LBP texture analysis operator is defined as a grayscale invariant texture measure, derived from a general definition of texture in a local neighborhood.…”

Section: Introductionmentioning

confidence: 99%

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Extraction of Texture Information from Fuzzy Run Length Matrix

Venkateswarlu¹,

Sujatha²,

Kumar³

2012

IJCA

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For a precise texture classification and analysis, a run length matrix is constructed on the Local Binary pattern using fuzzy principles in the present paper. The proposed Run Length Matrix on Fuzzy LBP (RLM-FLBP) overcomes the disadvantages of the previous run length methods of texture classification that exist in the literature. LBP is a widely used tool for texture classification based on local features. The LBP does not provide greater amount of discriminate information of the local structure and it has a various other disadvantages. The main disadvantage of LBP is, that it compares the centre pixel value with its neighbors to derive the one of the three possible values {0, 1, 2}. The basic drawback of this comparison is that it is very sensitive to noise. And a major contrast between the central pixel and its surroundings are easily resulted by the slight fluctuations above or below the value of the Centre Pixel (CP) and its surroundings. To overcome this problem and to represent the missing local information effectively in the LBP, the present study introduced the concept of fuzzy logic on LBP. This overcomes the problem related to noise and contrast. The proposed method initially converts the 3×3 neighborhood in to fuzzy LBP. In the second stage the proposed method constructs the Run Length Matrix on Fuzzy LBP (RLM-FLBP). On these RLM-FLBP texture features are evaluated for a precise texture classification.

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Visual‐based and object‐conscious image retrieval by block reallocation into object region

Mochizuki¹,

Kawai²,

Sano³

et al. 2016

IEEJ Transactions Elec Engng

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Visual-based' image retrieval based on the visual similarities over the entire image is one of the powerful and useful ways when targeting large volume content with inadequate annotation. Generally, conventional methods divide a query image and target images in the database into grid-shaped blocks and calculate the similarity based on image features by comparing each corresponding block straightforwardly. However, the method sometimes fails in terms of object-conscious retrieval when their backgrounds are almost the same but the only the object is different or the object's size and/or position is different. To solve the problem, we propose a new method featuring the reallocation of some blocks to the object region (OB-blocks) and a new similarity score by placing a weight on the OB-blocks, which are derived from the visual saliency map. Our proposed method could realize 'visual-based and object-conscious' image retrieval. We verify the effectiveness of this method through comparison experiments.

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Block-Based Methods for Image Retrieval Using Local Binary Patterns

Cited by 162 publications

References 18 publications

Large Scale Online Learning of Image Similarity through Ranking

Large Scale Online Learning of Image Similarity through Ranking

Extraction of Texture Information from Fuzzy Run Length Matrix

Visual‐based and object‐conscious image retrieval by block reallocation into object region

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