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In this paper, we propose a content-based image retrieval (CBIR) approach using color and texture features extracted from block truncation coding based on binary ant colony optimization (BACOBTC). First, we present a near-optimized common bitmap scheme for BTC. Then, we convert the image to two color quantizers and a bitmap image-utilizing BACOBTC. Subsequently, the color and texture features, i.e., the color histogram feature (CHF) and the bit pattern histogram feature (BHF) are extracted to measure the similarity between a query image and the target image in the database and retrieve the desired image. The performance of the proposed approach was compared with several former image-retrieval schemes. The results were evaluated in terms of Precision-Recall and Average Retrieval Rate, and they showed that our approach outperformed the referenced approaches.
Yan-Hong Chen; Chin-Chen Chang; Chia-Chen Lin; Cheng-Yi Hsu. Content-Based Color Image Retrieval Using Block Truncation Coding Based on Binary Ant Colony Optimization. Symmetry 2018, 11, 21 .
AMA StyleYan-Hong Chen, Chin-Chen Chang, Chia-Chen Lin, Cheng-Yi Hsu. Content-Based Color Image Retrieval Using Block Truncation Coding Based on Binary Ant Colony Optimization. Symmetry. 2018; 11 (1):21.
Chicago/Turabian StyleYan-Hong Chen; Chin-Chen Chang; Chia-Chen Lin; Cheng-Yi Hsu. 2018. "Content-Based Color Image Retrieval Using Block Truncation Coding Based on Binary Ant Colony Optimization." Symmetry 11, no. 1: 21.
This paper proposes a new electric load forecasting model by hybridizing the fuzzy time series (FTS) and global harmony search algorithm (GHSA) with least squares support vector machines (LSSVM), namely GHSA-FTS-LSSVM model. Firstly, the fuzzy c-means clustering (FCS) algorithm is used to calculate the clustering center of each cluster. Secondly, the LSSVM is applied to model the resultant series, which is optimized by GHSA. Finally, a real-world example is adopted to test the performance of the proposed model. In this investigation, the proposed model is verified using experimental datasets from the Guangdong Province Industrial Development Database, and results are compared against autoregressive integrated moving average (ARIMA) model and other algorithms hybridized with LSSVM including genetic algorithm (GA), particle swarm optimization (PSO), harmony search, and so on. The forecasting results indicate that the proposed GHSA-FTS-LSSVM model effectively generates more accurate predictive results.
Yan Hong Chen; Wei-Chiang Hong; Wen Shen; Ning Ning Huang. Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm. Energies 2016, 9, 70 .
AMA StyleYan Hong Chen, Wei-Chiang Hong, Wen Shen, Ning Ning Huang. Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm. Energies. 2016; 9 (2):70.
Chicago/Turabian StyleYan Hong Chen; Wei-Chiang Hong; Wen Shen; Ning Ning Huang. 2016. "Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm." Energies 9, no. 2: 70.