Multi-feature Content-based Product Image Retrieval Based on Region of Main Object Lunshao Chai 1 Honggang Zhang 1 Zhen Qin 2 Jie Yu 1 Yonggang Qi 1 1 Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing, China 2 Riverside Lab for Artificial Intelligence Research University of California, Riverside Riverside, CA, USA. AbstractContent-based image retrieval (CBIR) has got an intense interest and seen considerable progress over the last decade. But most of the time it is only applied in laboratory. One important reason for this is the diversity of images. Different practical situations call for different taxonomy definitions of images, and lead to very different solutions. At present, and even in the foreseeable future, a general purpose CBIR system is not really possible. However, image search engines oriented at specific domains are feasible in technology and also have the actual demand. With the rapid development of electronic commerce, searching specific product by image has become one of the most attractive related research topics. In this paper, we propose a region-based method fit for the content-based retrieval of product images. The method focuses on two key issues: fast extraction of the main region, in which the product locates, as well as efficient shape and color features extraction. To show the validity of the proposed region-based method, compared experiments are carried out and illustrated on the PI 100 dataset. Keywords-content-based product image retrieval; fuzzy color histogram; radial-harmonic-fourier moments (RHFMs); region of interest (ROI) I. INTRODUCTION Ever since the day CBIR emerged, it has been considered as a gold mine with the potential to change the way people search information. Using text for image retrieval is a mature technology, but in many cases, it is hard to get precise semantic description of what people search for. However, an image can depict an object or a scene clearly and roundly, and with CBIR, we can avoid information loss and distortion occurring during describing images. Although with a bright future, general purpose CBIR systems are still far from being utilized in real world applications. Nevertheless, we can expect CBIR to become practical in certain contexts of use in the near future. Content-based product image retrieval refers to image-to-image retrieval of product pictures, especially in the case of online shopping. It is a lively field of research driven by huge demand and most likely to make a breakthrough. CBIR of product images is technically possible largely due to the simplification of segmentation. Even though segmentation has always been a hotspot of study and new achievements deliver every year as in [1][2][3], extracting semantic ROI automatically and precisely is still beyond the reach of current computer vision techniques. However, there are some favorable restrictions in product images: background are rarely complex, and the main object - the product, will be distinct and composes the center of the image. These restrictions greatly simplify the segmentation and therefore image analysis is possible. In our work, Radial-Harmonic-Fourier Moments (RHFMs) is adopted to describe the shape of the product. Shape is one of the most important visual features, in that perceiving and distinguishing the shape of objects in an image are crucial for people to understand it. Comparing with lower level features - color and texture, shape belongs to middle level features, which are important components to describe high level visual features like target or object. Moment invariants are one kind of image features that can satisfy shifting, scaling, rotation, and intensity invariance. From the first time as moment invariants was introduced in [4], a number of variants for moments invariants have been proposed as in [5][6][7][8] and used to describe images [9][10][11]. RHFMs was first proposed in [12]. The analysis performed showed that RHFMs outperformed other known moments in image description, image reconstruction, and noise-resistance power, especially for small-size images. Fuzzy Color Histogram is used to describe the color feature of the image in our method. The technique of color histogram has been widely used because of its simplification and resistance to distortions such as rotation and scaling [13][14]. The fuzzy linking technique we choose is proposed in [15] as a component of Color and Edge Directivity Descriptor (CEDD). With low computational power needed, the system can generate an accurate and robust 24-bin color histogram representing 24 colors in an image. The rest of the paper is organized as follows. In Section II, we propose a fast extraction algorithm fit to product images. Using prior information, this algorithm can extract the main region, which contains the product, with much less time expense than traditional segmentation algorithms. In section III, the main region we extracted is used as region of interest (ROI). Based on the ROI, shape feature (Radial Harmonic Fourier Moments, RHFMs) and color feature (fuzzy color histogram) are then extracted. In section IV, we compare the performance of features extracted with our region-based method with four other commonly employed non-region-based features. Section V concludes and future work is discussed. This work was partially supported by National Natural Science Foundation of China under Grant No.61005004, the Fundamental Research Funds for the Central Universities and Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry. This work is also funded by Qualcomm, Inc. 978-1-4577-0031-6/11/$26.00 ©2011 TEEE TCTCS 2011