Content Based Image Retrieval by Convolutional Neural Networks

Hamreras S., Benítez-Rochel R., Boucheham B., Molina-Cabello M.A., López-Rubio E. (2019) Content Based Image Retrieval by Convolutional Neural Networks. In: Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science, vol 11487. Springer.In this paper, we present a Convolutional Neural Network (CNN) for feature extraction in Content based Image Retrieval (CBIR). The proposed CNN aims at reducing the semantic gap between low level and high-level features. Thus, improving retrieval results. Our CNN is the result of a transfer learning technique using Alexnet pretrained network. It learns how to extract representative features from a learning database and then uses this knowledge in query feature extraction. Experimentations performed on Wang (Corel 1K) database show a significant improvement in terms of precision over the state of the art classic approaches.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A Hybrid Approach for Improved Image Similarity Using Semantic Segmentation

International audienceContent Based Image Retrieval(CBIR) is the task of finding the images from the datasets that consider similar to the input query based on its visual characteristics. Several methods from the state of the art based on visual methods (Bag of visual words, VLAD, ...) or recent deep leaning methods try to solve the CBIR problem. In particular, Deep learning is a new field and used for several vision applications including CBIR. But, even with the increase of the performance of deep learning algorithms, this problem is still a challenge in computer vision. To tackle this problem, we present in this paper an efficient CBIR framework based on incorporation between deep learning based semantic segmentation and visual features. We show experimentally that the incorporate leads to the increase of accuracy of our CBIR framework. We study the performance of the proposed approach on four different datasets(Wang, MSRC V1,MSRC V2, Linnaeus

Image retrieval based on effective feature extraction and diffusion process

Feature extraction and its matching are two critical tasks in image retrieval. This paper presents a new methodology for content-based image retrieval by integrating three features, and then optimizing feature metric by diffusion process. To boost the discriminative power, the color histogram, local directional pattern, and dense SIFT features based on bag of features (BoF) are selected. Then diffusion process is applied to seek a global optimization for image matching based on fused multi-features. The diffusion process can capture the intrinsic manifold structure on a dataset, and thus enhance the overall retrieval performance significantly. Finally, a new search strategy is explored to make the diffusion process work even better when the number of retrieval images is small. In order to validate our proposed approach, four benchmark databases are used, and the results of experiments show that the proposed approach outperforms all other existing approaches

Development of an evolutionary fuzzy expert system for estimating future behavior of stock price

The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a ''data mining-based evolutionary fuzzy expert system'' (DEFES) approach to estimate the behavior of stock price. This tool is developed in seven-stage architecture. Data mining is used in three stages to reduce the complexity of the whole data space. The first stage, noise filtering, is used to make our raw data clean and smooth. Variable selection is second stage; we use stepwise regression analysis to choose the key variables been considered in the model. In the third stage, K-means is used to divide the data into subpopulations to decrease the effects of noise and rebate complexity of the patterns. At next stage, extraction of Mamdani type fuzzy rule-based system will be carried out for each cluster by means of genetic algorithm and evolutionary strategy. In the fifth stage, we use binary genetic algorithm to rule filtering to remove the redundant rules in order to solve over learning phenomenon. In the sixth stage, we utilize the genetic tuning process to slightly adjust the shape of the membership functions. Last stage is the testing performance of tool and adjusts parameters. This is the first study on using an approximate fuzzy rule base system and evolutionary strategy with the ability of extracting the whole knowledge base of fuzzy expert system for stock price forecasting problems. The superiority and applicability of DEFES are shown for International Business Machines Corporation and compared the outcome with the results of the other methods. Results with MAPE metric and Wilcoxon signed ranks test indicate that DEFES provides more accuracy and outperforms all previous methods, so it can be considered as a superior tool for stock price forecasting problems