Content Based Image Retrieval

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In recent years, instance-level-image retrieval has attracted massive attention. Several researchers proposed that the representations learned by convolution neural network (CNN) can be used for image retrieval task. In this study, the authors propose an effective feature encoder to extract robust information from CNN. It consists of two main steps: the embedding step and the aggregation step. Moreover, they apply the multi-task loss function to train their model in order to make the training process more effective. Finally, this study proposes a novel representation policy that encodes feature vectors extracted from different layers to capture both local patterns and semantic concepts from deep CNN. They call this ?multi-level-image representation?, which could further improve the performance. The proposed model is helpful to improve the retrieval performance. For the sake of comprehensively evaluating the performance of their approach, they conducted ablation experiments with various convolutional NN architectures. Furthermore, they apply their approach to a concrete challenge ? Alibaba large-scale search challenge. The results show that their model is effective and competitive.


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System Analysis

?? Existing Systems

  • SIFT based sparse features representation
  • GLCM and Local Binary Pattern analysis


  • Sparse representation doesn?t contain information about texture.
  • It has poor discriminatory power
  • It is poor to characterize the contrast information

Proposed Method

Robust object recognition for Content based image retrieval on multimedia images based on, Discriminative robust Local binary pattern and Local ternary Pattern analysis


  • Better efficiency and less sensitive to noise
  • Better accuracy
  • Processing time is less

Block diagram:


Content Based Image Retrieval

HSV Colour Mode?

The HSV stands for the Hue, Saturation, and worth supported the artists (Tint, Shade, and Tone). The frame of reference in an extremely hexatone in Figure a combine of. (a).

And Figure a combine of.(b) a browse of the HSV colour model. the price represents intensity of a colour, that’s decoupled from the colour information inside the drawn image.

The hue and saturation components area unit intimately related to the means that human eye perceives colour resulting in image method algorithms with physiological basis.

As hue varies from zero to one.0, the corresponding colours vary from red, through yellow, green, cyan, blue, and magenta, back to red, therefore there are actually red values every at zero and one.0.

As saturation varies from zero to one.0, the corresponding colours (hues) vary from unsaturated (shades of gray) to fully saturated (no white component). As value, or brightness, varies from zero to one.0, the corresponding colours become more and more brighter.

DRLBP Descriptor:

The descriptor local binary pattern is used to compare all the pixels including the center pixel with the neighboring pixels in the kernel to improve the robustness against the illumination variation.An LBP code for a neighborhood was produced by multiplying the threshold values with weights given to the corresponding pixels, and summing up the result. LBP codes are weighed using gradient vector to generate the histogram of robust LBP and discriminative features are determined from the robust local binary pattern codes. DRLBP is represented interms of set of normalized histogram bins as local texture features. It is used to discriminate the local edge texture of face invariant to changes of contrast and shape.?

Types of Neural Networks:

Artificial Neural Network

  • Back propagation networks
  • General Regression Neural Networks

DTREG implements the most widely used types of neural networks:

  1. a) Multilayer Perceptron Networks (also known as multilayer feed-forward network),
  2. b) Cascade Correlation Neural Networks,
  3. c) Back propagation networks (BPN)
  4. d) General Regression Neural Networks (GRNN)

This network has an input layer (on the left) with three neurons, one hidden layer (in the middle) with three neurons and an output layer (on the right) with three neurons.

There is one neuron in the input layer for each predictor variable. In the case of categorical variables, N-1 neurons are used to represent the N categories of the variable.

Hardware Requirements

  • system
  • 4 GB of RAM
  • 500 GB of Hard disk



[1] Settles, B., Craven, M., Ray, S.: ?Multiple-instance active learning?. Proc. Advances in Neural Information Processing Systems, Vancouver, Canada, 2007, pp. 1289?1296

[2] Gordo, A., et al.: ?Deep image retrieval: learning global representations for image search?. Proc. European Conf. on Computer Vision, Amsterdam, The Netherlands, October 2016, pp. 241?257

[3] Lowe, D.G.: ?Distinctive image features from scale-invariant keypoints?, Int. J. Comput. Vis., 2004, 60, (2), pp. 91?110

[4] Bay, H., Tuytelaars, T., Van Gool, L.: ?Surf: speeded up robust features?. Proc. European Conf. on Computer Vision, Graz, Austria, May 2006, pp. 404?417

[5] Qiu, G.: ?Indexing chromatic and achromatic patterns for content-based colour image retrieval?, Pattern Recognit., 2002, 35, (8), pp. 1675?1686

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